Publications (sorted in inverse chronological order)

2018

• Risch, J., Krestel, R.: Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom. Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING). p. 166--176 (2018).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the community engaged. Further, news providers have to make sure to not give the impression of censorship or dissemination of fake news. Yet manual moderation is very expensive and becomes more and more unfeasible with the increasing amount of comments. Hence, we propose a semi-automatic, holistic approach, which includes comment features but also their context, such as information about users and articles. For evaluation, we present experiments on a novel corpus of 3 million news comments annotated by a team of professional moderators.
  @inproceedings{risch2018delete, abstract = {Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the community engaged. Further, news providers have to make sure to not give the impression of censorship or dissemination of fake news. Yet manual moderation is very expensive and becomes more and more unfeasible with the increasing amount of comments. Hence, we propose a semi-automatic, holistic approach, which includes comment features but also their context, such as information about users and articles. For evaluation, we present experiments on a novel corpus of 3 million news comments annotated by a team of professional moderators.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING)}, keywords = {hpi comments_analysis web_science isg}, month = {August}, pages = {166--176}, title = {Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom}, year = 2018 }
• Bunk, S., Krestel, R.: WELDA: Enhancing Topic Models by Incorporating Local Word Contexts. Joint Conference on Digital Libraries (JCDL 2018). ACM, Forth Worth, Texas, USA (2018).
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  The distributional hypothesis states that similar words tend to have similar contexts in which they occur. Word embedding models exploit this hypothesis by learning word vectors based on the local context of words. Probabilistic topic models on the other hand utilize word co-occurrences across documents to identify topically related words. Due to their complementary nature, these models define different notions of word similarity, which, when combined, can produce better topical representations. In this paper we propose WELDA, a new type of topic model, which combines word embeddings (WE) with latent Dirichlet allocation (LDA) to improve topic quality. We achieve this by estimating topic distributions in the word embedding space and exchanging selected topic words via Gibbs sampling from this space. We present an extensive evaluation showing that WELDA cuts runtime by at least 30% while outperforming other combined approaches with respect to topic coherence and for solving word intrusion tasks.
  @inproceedings{krestel-jcdl18b, abstract = {The distributional hypothesis states that similar words tend to have similar contexts in which they occur. Word embedding models exploit this hypothesis by learning word vectors based on the local context of words. Probabilistic topic models on the other hand utilize word co-occurrences across documents to identify topically related words. Due to their complementary nature, these models define different notions of word similarity, which, when combined, can produce better topical representations. In this paper we propose WELDA, a new type of topic model, which combines word embeddings (WE) with latent Dirichlet allocation (LDA) to improve topic quality. We achieve this by estimating topic distributions in the word embedding space and exchanging selected topic words via Gibbs sampling from this space. We present an extensive evaluation showing that WELDA cuts runtime by at least 30% while outperforming other combined approaches with respect to topic coherence and for solving word intrusion tasks.}, author = {Bunk, Stefan and Krestel, Ralf}, booktitle = {Joint Conference on Digital Libraries (JCDL 2018)}, keywords = {topic_models web_science deep_learning isg}, month = {June 3--6}, publisher = {ACM}, title = {WELDA: Enhancing Topic Models by Incorporating Local Word Contexts}, year = 2018 }
• Risch, J., Krestel, R.: Learning Patent Speak: Investigating Domain-Specific Word Embeddings. Proceedings of the Thirteenth International Conference on Digital Information Management (ICDIM) (2018).
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  A patent examiner needs domain-specific knowledge to classify a patent application according to its field of invention. Standardized classification schemes help to compare a patent application to previously granted patents and thereby check its novelty. Due to the large volume of patents, automatic patent classification would be highly beneficial to patent offices and other stakeholders in the patent domain. However, a challenge for the automation of this costly manual task is the patent-specific language use. To facilitate this task, we present domain-specific pre-trained word embeddings for the patent domain. We trained our model on a very large dataset of more than 5 million patents to learn the language use in this domain. We evaluated the quality of the resulting embeddings in the context of patent classification. To this end, we propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings. Experiments on a standardized evaluation dataset show that our approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches.
  @inproceedings{risch2018learning, abstract = {A patent examiner needs domain-specific knowledge to classify a patent application according to its field of invention. Standardized classification schemes help to compare a patent application to previously granted patents and thereby check its novelty. Due to the large volume of patents, automatic patent classification would be highly beneficial to patent offices and other stakeholders in the patent domain. However, a challenge for the automation of this costly manual task is the patent-specific language use. To facilitate this task, we present domain-specific pre-trained word embeddings for the patent domain. We trained our model on a very large dataset of more than 5 million patents to learn the language use in this domain. We evaluated the quality of the resulting embeddings in the context of patent classification. To this end, we propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings. Experiments on a standardized evaluation dataset show that our approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the Thirteenth International Conference on Digital Information Management (ICDIM)}, keywords = {myown web_science deep_learning isg}, month = {September}, title = {Learning Patent Speak: Investigating Domain-Specific Word Embeddings}, year = 2018 }
• van Aken, B., Risch, J., Krestel, R., Löser, A.: Challenges for Toxic Comment Classification: An In-Depth Error Analysis. Proceedings of the 2nd Workshop on Abusive Language Online (co-located with EMNLP) (2018).
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  Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task’s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of- the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.
  @inproceedings{vanaken2018challenges, abstract = {Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task’s challenges others still remain unsolved and directions for further research are needed. To this end, we compare different approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of- the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.}, author = {van Aken, Betty and Risch, Julian and Krestel, Ralf and Löser, Alexander}, booktitle = {Proceedings of the 2nd Workshop on Abusive Language Online (co-located with EMNLP)}, keywords = {myown comments_analysis web_science isg}, month = {October}, title = {Challenges for Toxic Comment Classification: An In-Depth Error Analysis}, year = 2018 }
• Repke, T., Krestel, R., Edding, J., Hartmann, M., Hering, J., Kipping, D., Schmidt, H., Scordialo, N., Zenner, A.: Beacon in the Dark: A System for Interactive Exploration of Large Email Corpora. Proceedings of the International Conference on Information and Knowledge Management (CIKM). p. 1--4. ACM (2018).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Emails play a major role in today's business communication, documenting not only work but also decision making processes. The large amount of heterogeneous data in these email corpora renders manual investigations by experts infeasible. Auditors or jornalists, e.g., who are looking for irregular or inappropriate content or suspicous patterns, are in desperate need for computer-aided exploration tools to support their investigations. We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations for rearranging and aggregating the extracted information to reveal salient patterns.
  @inproceedings{noauthororeditor, abstract = {Emails play a major role in today's business communication, documenting not only work but also decision making processes. The large amount of heterogeneous data in these email corpora renders manual investigations by experts infeasible. Auditors or jornalists, e.g., who are looking for irregular or inappropriate content or suspicous patterns, are in desperate need for computer-aided exploration tools to support their investigations. We present our Beacon system for the exploration of such corpora at different levels of detail. A distributed processing pipeline combines text mining methods and social network analysis to augment the already semi-structured nature of emails. The user interface ties into the resulting cleaned and enriched dataset. For the interface design we identify three objectives expert users have: gain an initial overview of the data to identify leads to investigate, understand the context of the information at hand, and have meaningful filters to iteratively focus onto a subset of emails. To this end we make use of interactive visualisations for rearranging and aggregating the extracted information to reveal salient patterns.}, author = {Repke, Tim and Krestel, Ralf and Edding, Jakob and Hartmann, Moritz and Hering, Jonas and Kipping, Dennis and Schmidt, Hendrik and Scordialo, Nico and Zenner, Alexander}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM)}, keywords = {hpi exploration business_communication web_science isg bca}, month = {oct}, pages = {1--4}, publisher = {ACM}, title = {Beacon in the Dark: A System for Interactive Exploration of Large Email Corpora}, year = 2018 }
• Risch, J., Krebs, E., Löser, A., Riese, A., Krestel, R.: Fine-Grained Classification of Offensive Language. Proceedings of GermEval (co-located with KONVENS). p. 38--44 (2018).
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  Social media platforms receive massive amounts of user-generated content that may include offensive text messages. In the context of the GermEval task 2018, we propose an approach for fine-grained classification of offensive language. Our approach comprises a Naive Bayes classifier, a neural network, and a rule-based approach that categorize tweets. In addition, we combine the approaches in an ensemble to overcome weaknesses of the single models. We cross-validate our approaches with regard to macro-average F1-score on the provided training dataset.
  @inproceedings{risch2018finegrained, abstract = {Social media platforms receive massive amounts of user-generated content that may include offensive text messages. In the context of the GermEval task 2018, we propose an approach for fine-grained classification of offensive language. Our approach comprises a Naive Bayes classifier, a neural network, and a rule-based approach that categorize tweets. In addition, we combine the approaches in an ensemble to overcome weaknesses of the single models. We cross-validate our approaches with regard to macro-average F1-score on the provided training dataset.}, author = {Risch, Julian and Krebs, Eva and Löser, Alexander and Riese, Alexander and Krestel, Ralf}, booktitle = {Proceedings of GermEval (co-located with KONVENS)}, keywords = {myown comments_analysis web_science isg}, month = {September}, pages = {38--44}, title = {Fine-Grained Classification of Offensive Language}, year = 2018 }
• Exeler, C., Graber, M., Junge, T., Ramson, S., Ramson, C., Tschirschnitz, F., Naumann, F.: Piggyback Profiling: Enhancing Query Results with Metadata. Lernen. Wissen. Daten. Analysen. (LWDA) (2018).
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  @inproceedings{exeler2018piggyback, author = {Exeler, Claudia and Graber, Maria and Junge, Tino and Ramson, Stefan and Ramson, Cathleen and Tschirschnitz, Fabian and Naumann, Felix}, booktitle = {Lernen. Wissen. Daten. Analysen. (LWDA)}, keywords = {myown}, title = {Piggyback Profiling: Enhancing Query Results with Metadata}, year = 2018 }
• Risch, J., Krestel, R.: Aggression Identification Using Deep Learning and Data Augmentation. Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING). p. 150--158 (2018).
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  Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and — at an extreme level — is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named “Julian” achieves an F1-score of 60% on both English datasets, 63% on the Hindi Facebook dataset, and 38% on the Hindi Twitter dataset.
  @inproceedings{risch2018aggression, abstract = {Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and — at an extreme level — is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named “Julian” achieves an F1-score of 60% on both English datasets, 63% on the Hindi Facebook dataset, and 38% on the Hindi Twitter dataset.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING)}, keywords = {hpi comments_analysis web_science isg}, month = {August}, pages = {150--158}, title = {Aggression Identification Using Deep Learning and Data Augmentation}, year = 2018 }
• Risch, J., Krestel, R.: My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections. Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL). p. 283--292 (2018).
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  Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.
  @inproceedings{risch2018approach, abstract = {Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL)}, keywords = {hpi topic_models web_science isg}, pages = {283--292}, title = {My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections}, year = 2018 }
• Ambroselli, C., Risch, J., Krestel, R., Loos, A.: Prediction for the Newsroom: Which Articles Will Get the Most Comments? Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). p. 193--199. ACL, New Orleans, Louisiana, USA (2018).
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  The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.
  @inproceedings{krestel-naacl18, abstract = {The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.}, author = {Ambroselli, Carl and Risch, Julian and Krestel, Ralf and Loos, Andreas}, booktitle = {Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, keywords = {hpi comments_analysis web_science isg}, month = {June 1--6}, pages = {193--199}, publisher = {ACL}, title = {Prediction for the Newsroom: Which Articles Will Get the Most Comments?}, year = 2018 }
• Lazaridou, K., Gruetze, T., Naumann, F.: Where in the World Is Carmen Sandiego? Detecting Person Locations via Social Media Discussions. Proceedings of the ACM Conference on Web Science. ACM (2018).
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  In today's social media, news often spread faster than in mainstream media, along with additional context and aspects about the current affairs. Consequently, users in social networks are up-to-date with the details of real-world events and the involved individuals. Examples include crime scenes and potential perpetrator descriptions, public gatherings with rumors about celebrities among the guests, rallies by prominent politicians, concerts by musicians, etc. We are interested in the problem of tracking persons mentioned in social media, namely detecting the locations of individuals by leveraging the online discussions about them. Existing literature focuses on the well-known and more convenient problem of user location detection in social media, mainly as the location discovery of the user profiles and their messages. In contrast, we track individuals with text mining techniques, regardless whether they hold a social network account or not. We observe what the community shares about them and estimate their locations. Our approach consists of two steps: firstly, we introduce a noise filter that prunes irrelevant posts using a recursive partitioning technique. Secondly, we build a model that reasons over the set of messages about an individual and determines his/her locations. In our experiments, we successfully trace the last U.S. presidential candidates through millions of tweets published from November 2015 until January 2017. Our results outperform previously introduced techniques and various baselines.
  @inproceedings{lazaridou2018where, abstract = {In today's social media, news often spread faster than in mainstream media, along with additional context and aspects about the current affairs. Consequently, users in social networks are up-to-date with the details of real-world events and the involved individuals. Examples include crime scenes and potential perpetrator descriptions, public gatherings with rumors about celebrities among the guests, rallies by prominent politicians, concerts by musicians, etc. We are interested in the problem of tracking persons mentioned in social media, namely detecting the locations of individuals by leveraging the online discussions about them. Existing literature focuses on the well-known and more convenient problem of user location detection in social media, mainly as the location discovery of the user profiles and their messages. In contrast, we track individuals with text mining techniques, regardless whether they hold a social network account or not. We observe what the community shares about them and estimate their locations. Our approach consists of two steps: firstly, we introduce a noise filter that prunes irrelevant posts using a recursive partitioning technique. Secondly, we build a model that reasons over the set of messages about an individual and determines his/her locations. In our experiments, we successfully trace the last U.S. presidential candidates through millions of tweets published from November 2015 until January 2017. Our results outperform previously introduced techniques and various baselines.}, author = {Lazaridou, Konstantina and Gruetze, Toni and Naumann, Felix}, booktitle = {Proceedings of the ACM Conference on Web Science}, keywords = {web_science social_media isg}, month = {may}, publisher = {ACM}, series = {10th}, title = {Where in the World Is Carmen Sandiego? Detecting Person Locations via Social Media Discussions}, year = 2018 }
• Berti-Equille, L., Harmouch, H., Naumann, F., Novelli, N., Thirumuruganathan, S.: Discovery of Genuine Functional Dependencies from Relational Data with Missing Values. Proceedings of the VLDB Endowment (PVLDB). pp. 880-892 (2018).
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  Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.
  @inproceedings{bertiequille2018discovery, abstract = {Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.}, author = {Berti-Equille, Laure and Harmouch, Hazar and Naumann, Felix and Novelli, Noel and Thirumuruganathan, Saravanan}, booktitle = {Proceedings of the VLDB Endowment (PVLDB)}, keywords = {null profiling discovery missing isg functional_dependencies}, month = 4, number = 8, pages = {880-892}, title = {Discovery of Genuine Functional Dependencies from Relational Data with Missing Values}, volume = 11, year = 2018 }
• Repke, T., Krestel, R.: Topic-aware Network Visualisation to Explore Large Email Corpora. International Workshop on Big Data Visual Exploration and Analytics (BigVis). CEUR-WS.org (2018).
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  Nowadays, more and more large datasets exhibit an intrinsic graph structure. While there exist special graph databases to handle ever increasing amounts of nodes and edges, visualising this data becomes infeasible quickly with growing data. In addition, looking at its structure is not sufficient to get an overview of a graph dataset. Indeed, visualising additional information about nodes or edges without cluttering the screen is essential. In this paper, we propose an interactive visualisation for social networks that positions individuals (nodes) on a two-dimensional canvas such that communities defined by social links (edges) are easily recognisable. Furthermore, we visualise topical relatedness between individuals by analysing information about social links, in our case email communication. To this end, we utilise document embeddings, which project the content of an email message into a high dimensional semantic space and graph embeddings, which project nodes in a network graph into a latent space reflecting their relatedness.
  @inproceedings{repke-bigvis2018, abstract = {Nowadays, more and more large datasets exhibit an intrinsic graph structure. While there exist special graph databases to handle ever increasing amounts of nodes and edges, visualising this data becomes infeasible quickly with growing data. In addition, looking at its structure is not sufficient to get an overview of a graph dataset. Indeed, visualising additional information about nodes or edges without cluttering the screen is essential. In this paper, we propose an interactive visualisation for social networks that positions individuals (nodes) on a two-dimensional canvas such that communities defined by social links (edges) are easily recognisable. Furthermore, we visualise topical relatedness between individuals by analysing information about social links, in our case email communication. To this end, we utilise document embeddings, which project the content of an email message into a high dimensional semantic space and graph embeddings, which project nodes in a network graph into a latent space reflecting their relatedness.}, author = {Repke, Tim and Krestel, Ralf}, booktitle = {International Workshop on Big Data Visual Exploration and Analytics (BigVis)}, keywords = {business_communication web_science deep_learning isg}, month = {mar}, publisher = {CEUR-WS.org}, series = {Workshop Proceedings of the {EDBT/ICDT} 2018 Joint Conference}, title = {Topic-aware Network Visualisation to Explore Large Email Corpora}, year = 2018 }
• Repke, T., Krestel, R.: Bringing Back Structure to Free Text Email Conversations with Recurrent Neural Networks. 40th European Conference on Information Retrieval (ECIR 2018). Springer, Grenoble, France (2018).
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  Email communication plays an integral part of everybody's life nowadays. Especially for business emails, extracting and analysing these communication networks can reveal interesting patterns of processes and decision making within a company. Fraud detection is another application area where precise detection of communication networks is essential. In this paper we present an approach based on recurrent neural networks to untangle email threads originating from forward and reply behaviour. We further classify parts of emails into 2 or 5 zones to capture not only header and body information but also greetings and signatures. We show that our deep learning approach outperforms state-of-the-art systems based on traditional machine learning and hand-crafted rules. Besides using the well-known Enron email corpus for our experiments, we additionally created a new annotated email benchmark corpus from Apache mailing lists.
  @inproceedings{repke-ecir18, abstract = {Email communication plays an integral part of everybody's life nowadays. Especially for business emails, extracting and analysing these communication networks can reveal interesting patterns of processes and decision making within a company. Fraud detection is another application area where precise detection of communication networks is essential. In this paper we present an approach based on recurrent neural networks to untangle email threads originating from forward and reply behaviour. We further classify parts of emails into 2 or 5 zones to capture not only header and body information but also greetings and signatures. We show that our deep learning approach outperforms state-of-the-art systems based on traditional machine learning and hand-crafted rules. Besides using the well-known Enron email corpus for our experiments, we additionally created a new annotated email benchmark corpus from Apache mailing lists.}, author = {Repke, Tim and Krestel, Ralf}, booktitle = {40th European Conference on Information Retrieval (ECIR 2018)}, keywords = {business_communication web_science deep_learning isg}, month = {March 26--29}, publisher = {Springer}, title = {Bringing Back Structure to Free Text Email Conversations with Recurrent Neural Networks}, year = 2018 }
• Risch, J., Garda, S., Krestel, R.: Book Recommendation Beyond the Usual Suspects: Embedding Book Plots Together with Place and Time Information. Proceedings of the 20th International Conference On Asia-Pacific Digital Libraries (ICADL) (2018).
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  Content-based recommendation of books and other media is usually based on semantic similarity measures. While metadata can be compared easily, measuring the semantic similarity of narrative literature is challenging. Keyword-based approaches are biased to retrieve books of the same series or do not retrieve any results at all in sparser libraries. We propose to represent plots with dense vectors to foster semantic search for similar plots even if they do not have any words in common. Further, we propose to embed plots, places, and times in the same embedding space. Thereby, we allow arithmetics on these aspects. For example, a book with a similar plot but set in a different, user-specified place can be retrieved. We evaluate our findings on a set of 16,000 book synopses that spans literature from 500 years and 200 genres and compare our approach to a keyword-based baseline.
  @inproceedings{noauthororeditor2018recommendation, abstract = {Content-based recommendation of books and other media is usually based on semantic similarity measures. While metadata can be compared easily, measuring the semantic similarity of narrative literature is challenging. Keyword-based approaches are biased to retrieve books of the same series or do not retrieve any results at all in sparser libraries. We propose to represent plots with dense vectors to foster semantic search for similar plots even if they do not have any words in common. Further, we propose to embed plots, places, and times in the same embedding space. Thereby, we allow arithmetics on these aspects. For example, a book with a similar plot but set in a different, user-specified place can be retrieved. We evaluate our findings on a set of 16,000 book synopses that spans literature from 500 years and 200 genres and compare our approach to a keyword-based baseline.}, author = {Risch, Julian and Garda, Samuele and Krestel, Ralf}, booktitle = {Proceedings of the 20th International Conference On Asia-Pacific Digital Libraries (ICADL)}, keywords = {hpi myown web_science}, month = {November}, title = {Book Recommendation Beyond the Usual Suspects: Embedding Book Plots Together with Place and Time Information}, year = 2018 }
• Bornemann, L., Bleifuß, T., Kalashnikov, D., Naumann, F., Srivastava, D.: Data Change Exploration using Time Series Clustering. Datenbank-Spektrum. 18, 1-9 (2018).
  [ Abstract ] [ BibTeX ] [ URL ]
   
  Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB. The changes to the data are captured as events, which we call change records. In order to extract temporal behavior we count changes in time periods and propose a general transformation framework that aggregates groups of changes to numerical time series of different resolutions. We use these time series to study different application scenarios of unsupervised clustering. Our explorative results show that changes made to collaboratively edited data sources can help find characteristic behavior, distinguish entities or properties and provide insight into the respective domains.
  @article{bornemann2018change, abstract = {Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB. The changes to the data are captured as events, which we call change records. In order to extract temporal behavior we count changes in time periods and propose a general transformation framework that aggregates groups of changes to numerical time series of different resolutions. We use these time series to study different application scenarios of unsupervised clustering. Our explorative results show that changes made to collaboratively edited data sources can help find characteristic behavior, distinguish entities or properties and provide insight into the respective domains.}, author = {Bornemann, Leon and Bleifuß, Tobias and Kalashnikov, Dmitri and Naumann, Felix and Srivastava, Divesh}, editor = {Michel, Sebastian and Gemulla, Rainer and Schenkel, Ralf}, journal = {Datenbank-Spektrum}, keywords = {change_exploration hpi time_series clustering}, month = {may}, number = 2, pages = {1-9}, title = {Data Change Exploration using Time Series Clustering}, type = {Publication}, volume = 18, year = 2018 }
• Kruse, S., Naumann, F.: Efficient Discovery of Approximate Dependencies. Proceedings of the VLDB Endowment. 11, 759-772 (2018).
  [ BibTeX ] [ Download ]
   
  @article{kruse2018efficient, author = {Kruse, Sebastian and Naumann, Felix}, journal = {Proceedings of the VLDB Endowment}, keywords = {dependency_discovery data_profiling isg}, number = 7, pages = {759-772}, title = {Efficient Discovery of Approximate Dependencies}, volume = 11, year = 2018 }
• Agrawal, D., Chawla, S., Kaoudi, Z., Kruse, S., Quiané-Ruiz, J.A., Contreras-Rojas, B., Elmagarmid, A., Idris, Y., Lucas, J., Mansour, E., Ouzzani, M., Papotti, P., Tang, N., Thirumuruganathan, S., Troudi, A.: RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -. Proceedings of the VLDB Endowment (PVLDB). 11, (2018).
  [ Abstract ] [ BibTeX ]
   
  Solving business problems increasingly requires going beyond the limits of a single data processing platform (platform for short), such as Hadoop or a DBMS. As a result, organizations typically perform tedious and costly tasks to juggle their code and data across different platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging: finding the most efficient platform for a given task requires quite good expertise for all the available platforms. We present Rheem, a general-purpose cross-platform data processing system that decouples applications from the underlying platforms. It not only determines the best platform to run an incoming task, but also splits the task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). It features (i) a robust interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Using different real-world applications with Rheem, we demonstrate how cross-platform data processing can accelerate performance by more than one order of magnitude compared to single-platform data processing.
  @article{agrawal2018rheem, abstract = {Solving business problems increasingly requires going beyond the limits of a single data processing platform (platform for short), such as Hadoop or a DBMS. As a result, organizations typically perform tedious and costly tasks to juggle their code and data across different platforms. Addressing this pain and achieving automatic cross-platform data processing is quite challenging: finding the most efficient platform for a given task requires quite good expertise for all the available platforms. We present Rheem, a general-purpose cross-platform data processing system that decouples applications from the underlying platforms. It not only determines the best platform to run an incoming task, but also splits the task into subtasks and assigns each subtask to a specific platform to minimize the overall cost (e.g., runtime or monetary cost). It features (i) a robust interface to easily compose data analytic tasks; (ii) a novel cost-based optimizer able to find the most efficient platform in almost all cases; and (iii) an executor to efficiently orchestrate tasks over different platforms. As a result, it allows users to focus on the business logic of their applications rather than on the mechanics of how to compose and execute them. Using different real-world applications with Rheem, we demonstrate how cross-platform data processing can accelerate performance by more than one order of magnitude compared to single-platform data processing.}, author = {Agrawal, Divy and Chawla, Sanjay and Kaoudi, Zoi and Kruse, Sebastian and Quiané-Ruiz, Jorge Arnulfo and Contreras-Rojas, Bertty and Elmagarmid, Ahmed and Idris, Yasser and Lucas, Ji and Mansour, Essam and Ouzzani, Mourad and Papotti, Paolo and Tang, Nan and Thirumuruganathan, Saravanan and Troudi, Anis}, journal = {Proceedings of the VLDB Endowment (PVLDB)}, keywords = {rheem myown isg}, number = 11, title = {RHEEM: Enabling Cross-Platform Data Processing - May The Big Data Be With You! -}, volume = 11, year = 2018 }
• Sadiq, S., Dasu, T., Dong, X.L., Freire, J., Ilyas, I.F., Link, S., Miller, R.J., Naumann, F., Zhou, X., Srivastava, D.: Data Quality – The Role of Empiricism. SIGMOD Record. 46, 35-43 (2018).
  [ BibTeX ] [ Download ]
   
  @article{NaumannDataQualiyt18, author = {Sadiq, Shazia and Dasu, Tamraparni and Dong, Xin Luna and Freire, Juliana and Ilyas, Ihab F. and Link, Sebastian and Miller, Renée J. and Naumann, Felix and Zhou, Xiaofang and Srivastava, Divesh}, journal = {SIGMOD Record}, keywords = {data_quality isg}, number = 4, pages = {35-43}, title = {Data Quality – The Role of Empiricism}, volume = 46, year = 2018 }
• Bleifuß, T., Bornemann, L., Johnson, T., Kalashnikov, D.V., Naumann, F., Srivastava, D.: Exploring Change - A New Dimension of Data Analytics. Proceedings of the VLDB Endowment (PVLDB). 12, (2018).
  [ Abstract ] [ BibTeX ]
   
  Data and metadata in datasets experience many different kinds of change. Values are inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, etc. In such a dynamic situation, users might have many questions related to changes in the dataset, for instance which parts of the data are trustworthy and which are not? Users will wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? Is data cleansing required? The fact that data changed can hint at different hidden processes or agendas: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. We show various use cases that benefit from recognizing and exploring such change. We envision a system and methods to interactively explore such change, addressing the variability dimension of big data challenges. To this end, we propose a model to capture change and the process of exploring dynamic data to identify salient changes. We provide exploration primitives along with motivational examples and measures for the volatility of data. We identify technical challenges that need to be addressed to make our vision a reality, and propose directions of future work for the data management community.
  @article{bleifuss2018acceptedexploring, abstract = {Data and metadata in datasets experience many different kinds of change. Values are inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, etc. In such a dynamic situation, users might have many questions related to changes in the dataset, for instance which parts of the data are trustworthy and which are not? Users will wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? Is data cleansing required? The fact that data changed can hint at different hidden processes or agendas: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. We show various use cases that benefit from recognizing and exploring such change. We envision a system and methods to interactively explore such change, addressing the variability dimension of big data challenges. To this end, we propose a model to capture change and the process of exploring dynamic data to identify salient changes. We provide exploration primitives along with motivational examples and measures for the volatility of data. We identify technical challenges that need to be addressed to make our vision a reality, and propose directions of future work for the data management community.}, author = {Bleifuß, Tobias and Bornemann, Leon and Johnson, Theodore and Kalashnikov, Dmitri V. and Naumann, Felix and Srivastava, Divesh}, journal = {Proceedings of the VLDB Endowment (PVLDB)}, keywords = {isg}, note = {to appear}, number = 2, title = {Exploring Change - A New Dimension of Data Analytics}, volume = 12, year = 2018 }

2017

• Risch, J., Krestel, R.: What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers. Proceedings of the International Conference on Theory and Practice of Digital Libraries (TPDL). pp. 40-46 (2017).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Research results manifest in large corpora of patents and scientific papers. However, both corpora lack a consistent taxonomy and references across different document types are sparse. Therefore, and because of contrastive, domain-specific language, recommending similar papers for a given patent (or vice versa) is challenging. We propose a hybrid recommender system that leverages topic distributions and key terms to recommend related work despite these challenges. As a case study, we evaluate our approach on patents and papers of two fields: medical and computer science. We find that topic-based recommenders complement term-based recommenders for documents with collection-specific language and increase mean average precision by up to 23%. As a result of our work, publications from both corpora form a joint digital library, which connects academia and industry.
  @inproceedings{risch2017what, abstract = {Research results manifest in large corpora of patents and scientific papers. However, both corpora lack a consistent taxonomy and references across different document types are sparse. Therefore, and because of contrastive, domain-specific language, recommending similar papers for a given patent (or vice versa) is challenging. We propose a hybrid recommender system that leverages topic distributions and key terms to recommend related work despite these challenges. As a case study, we evaluate our approach on patents and papers of two fields: medical and computer science. We find that topic-based recommenders complement term-based recommenders for documents with collection-specific language and increase mean average precision by up to 23%. As a result of our work, publications from both corpora form a joint digital library, which connects academia and industry.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the International Conference on Theory and Practice of Digital Libraries (TPDL)}, keywords = {topic_models web_science isg}, pages = {40-46}, title = {What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers}, year = 2017 }
• Kruse, S., Hahn, D., Walter, M., Naumann, F.: Metacrate: Organize and Analyze Millions of Data Profiles. Proceedings of the International Conference on Information and Knowledge Management (CIKM). pp. 2483-2486. ACM (2017).
  [ BibTeX ] [ Download ]
   
  @inproceedings{kruse2017metacrate, author = {Kruse, Sebastian and Hahn, David and Walter, Marius and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM)}, keywords = {myown isg}, month = {nov}, organization = {ACM}, pages = {2483-2486}, title = {Metacrate: Organize and Analyze Millions of Data Profiles}, year = 2017 }
• Lazaridou, K., Krestel, R., Naumann, F.: Identifying Media Bias by Analyzing Reported Speech. International Conference on Data Mining. IEEE (2017).
  [ Abstract ] [ BibTeX ]
   
  Media analysis can reveal interesting patterns in the way newspapers report the news and how these patterns evolve over time. One example pattern is the quoting choices that media make, which could be used as bias indicators. Media slant can be expressed both with the choice of reporting an event, e.g. a person's statement, but also with the words used to describe the event. Thus, automatic discovery of systematic quoting patterns in the news could illustrate to the readers the media' beliefs, such as political preferences. In this paper, we aim to discover political media bias by demonstrating systematic patterns of reporting speech in two major British newspapers. To this end, we analyze news articles from 2000 to 2015. By taking into account different kinds of bias, such as selection, coverage and framing bias, we show that the quoting patterns of newspapers are predictable.
  @inproceedings{lazaridou2017identifying, abstract = {Media analysis can reveal interesting patterns in the way newspapers report the news and how these patterns evolve over time. One example pattern is the quoting choices that media make, which could be used as bias indicators. Media slant can be expressed both with the choice of reporting an event, e.g. a person's statement, but also with the words used to describe the event. Thus, automatic discovery of systematic quoting patterns in the news could illustrate to the readers the media' beliefs, such as political preferences. In this paper, we aim to discover political media bias by demonstrating systematic patterns of reporting speech in two major British newspapers. To this end, we analyze news articles from 2000 to 2015. By taking into account different kinds of bias, such as selection, coverage and framing bias, we show that the quoting patterns of newspapers are predictable.}, author = {Lazaridou, Konstantina and Krestel, Ralf and Naumann, Felix}, booktitle = {International Conference on Data Mining}, keywords = {news_analysis web_science isg}, publisher = {IEEE}, title = {Identifying Media Bias by Analyzing Reported Speech}, year = 2017 }
• Maschler, F., Niephaus, F., Risch, J.: Real or Fake? Large-Scale Validation of Identity Leaks. 47. Jahrestagung der Gesellschaft für Informatik (INFORMATIK). pp. 2437-2448 (2017).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  On the Internet, criminal hackers frequently leak identity data on a massive scale. Subsequent criminal activities, such as identity theft and misuse, put Internet users at risk. Leak checker services enable users to check whether their personal data has been made public. However, automatic crawling and identification of leak data is error-prone for different reasons. Based on a dataset of more than 180 million leaked identity records, we propose a software system that identifies and validates identity leaks to improve leak checker services. Furthermore, we present a proficient assessment of leak data quality and typical characteristics that distinguish valid and invalid leaks.
  @inproceedings{maschler2017largescale, abstract = {On the Internet, criminal hackers frequently leak identity data on a massive scale. Subsequent criminal activities, such as identity theft and misuse, put Internet users at risk. Leak checker services enable users to check whether their personal data has been made public. However, automatic crawling and identification of leak data is error-prone for different reasons. Based on a dataset of more than 180 million leaked identity records, we propose a software system that identifies and validates identity leaks to improve leak checker services. Furthermore, we present a proficient assessment of leak data quality and typical characteristics that distinguish valid and invalid leaks.}, author = {Maschler, Fabian and Niephaus, Fabio and Risch, Julian}, booktitle = {47. Jahrestagung der Gesellschaft für Informatik (INFORMATIK)}, keywords = {hpi}, pages = {2437-2448}, title = {Real or Fake? Large-Scale Validation of Identity Leaks}, year = 2017 }
• Zuo, Z., Loster, M., Krestel, R., Naumann, F.: Uncovering Business Relationships: Context-sensitive Relationship Extraction for Difficult Relationship Types. Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (LWDA) (2017).
  [ Abstract ] [ BibTeX ]
   
  This paper establishes a semi-supervised strategy for extracting various types of complex business relationships from textual data by using only a few manually provided company seed pairs that exemplify the target relationship. Additionally, we offer a solution for determining the direction of asymmetric relationships, such as “ownership of”. We improve the reliability of the extraction process by using a holistic pattern identification method that classifies the generated extraction patterns. Our experiments show that we can accurately and reliably extract new entity pairs occurring in the target relationship by using as few as five labeled seed pairs.
  @inproceedings{zuo2017re, abstract = {This paper establishes a semi-supervised strategy for extracting various types of complex business relationships from textual data by using only a few manually provided company seed pairs that exemplify the target relationship. Additionally, we offer a solution for determining the direction of asymmetric relationships, such as “ownership of”. We improve the reliability of the extraction process by using a holistic pattern identification method that classifies the generated extraction patterns. Our experiments show that we can accurately and reliably extract new entity pairs occurring in the target relationship by using as few as five labeled seed pairs.}, author = {Zuo, Zhe and Loster, Michael and Krestel, Ralf and Naumann, Felix}, booktitle = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (LWDA)}, keywords = {business_communication web_science isg}, month = {September}, title = {Uncovering Business Relationships: Context-sensitive Relationship Extraction for Difficult Relationship Types}, year = 2017 }
• Krestel, R., Risch, J.: How Do Search Engines Work? A Massive Open Online Course with 4000 Participants. Proceedings of the Conference Lernen, Wissen, Daten, Analysen. pp. 259-271 (2017).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Massive Open Online Courses (MOOCs) have introduced a new form of education. With thousands of participants per course, lectur- ers are confronted with new challenges in the teaching process. In this pa- per, we describe how we conducted an introductory information retrieval course for participants from all ages and educational backgrounds. We analyze different course phases and compare our experiences with regular on-site information retrieval courses at university.
  @inproceedings{krestel2017search, abstract = {Massive Open Online Courses (MOOCs) have introduced a new form of education. With thousands of participants per course, lectur- ers are confronted with new challenges in the teaching process. In this pa- per, we describe how we conducted an introductory information retrieval course for participants from all ages and educational backgrounds. We analyze different course phases and compare our experiences with regular on-site information retrieval courses at university.}, author = {Krestel, Ralf and Risch, Julian}, booktitle = {Proceedings of the Conference Lernen, Wissen, Daten, Analysen}, keywords = {web_science social_media isg}, month = {September}, pages = {259-271}, title = {How Do Search Engines Work? A Massive Open Online Course with 4000 Participants}, year = 2017 }
• Loster, M., Zuo, Z., Naumann, F., Maspfuhl, O., Thomas, D.: Improving Company Recognition from Unstructured Text by using Dictionaries. Proceedings of the International Conference on Extending Database Technology (EDBT) (2017).
  [ Abstract ] [ BibTeX ]
   
  While named entity recognition is a much addressed research topic, recognizing companies in text is of particular difficulty. Company names are extremely heterogeneous in structure, a given company can be referenced in many different ways, their names include person names, locations, acronyms, numbers, and other unusual tokens. Further, instead of using the official company name, quite different colloquial names are frequently used by the general public. We present a machine learning (CRF) system that reliably recognizes organizations in German texts. In particular, we construct and employ various dictionaries, regular expressions, text context, and other techniques to improve the results. In our experiments we achieved a precision of 91.11% and a recall of 78.82%, showing significant improvement over related work. Using our system we were able to extract 263,846 company mentions from a corpus of 141,970 newspaper articles.
  @inproceedings{Michael2017a, abstract = {While named entity recognition is a much addressed research topic, recognizing companies in text is of particular difficulty. Company names are extremely heterogeneous in structure, a given company can be referenced in many different ways, their names include person names, locations, acronyms, numbers, and other unusual tokens. Further, instead of using the official company name, quite different colloquial names are frequently used by the general public. We present a machine learning (CRF) system that reliably recognizes organizations in German texts. In particular, we construct and employ various dictionaries, regular expressions, text context, and other techniques to improve the results. In our experiments we achieved a precision of 91.11% and a recall of 78.82%, showing significant improvement over related work. Using our system we were able to extract 263,846 company mentions from a corpus of 141,970 newspaper articles.}, author = {Loster, Michael and Zuo, Zhe and Naumann, Felix and Maspfuhl, Oliver and Thomas, Dirk}, journal = {Proceedings of the International Conference on Extending Database Technology (EDBT)}, keywords = {companies conditional_random_fields CRF company_names recognition NER isg named_entity_recognition}, title = {Improving Company Recognition from Unstructured Text by using Dictionaries}, year = 2017 }
• Abedjan, Z., Golab, L., Naumann, F.: Data Profiling (tutorial). Proceedings of the International Conference on Management of Data (SIGMOD) (2017).
  [ BibTeX ]
   
  @inproceedings{DataProfilingTutorialSIGMOD17, author = {Abedjan, Ziawasch and Golab, Lukasz and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Management of Data (SIGMOD)}, keywords = {isg}, title = {Data Profiling (tutorial)}, year = 2017 }
• Papenbrock, T., Naumann, F.: Data-driven Schema Normalization. Proceedings of the International Conference on Extending Database Technology (EDBT). pp. 342-353 (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Ensuring Boyce-Codd Normal Form (BCNF) is the most popular way to remove redundancy and anomalies from datasets. Normalization to BCNF forces functional dependencies (FDs) into keys and foreign keys, which eliminates duplicate values and makes data constraints explicit. Despite being well researched in theory, converting the schema of an existing dataset into BCNF is still a complex, manual task, especially because the number of functional dependencies is huge and deriving keys and foreign keys is NP-hard. In this paper, we present a novel normalization algorithm called Normalize, which uses discovered functional dependencies to normalize relational datasets into BCNF. Normalize runs entirely data-driven, which means that redundancy is removed only where it can be observed, and it is (semi-)automatic, which means that a user may or may not interfere with the normalization process. The algorithm introduces an efficient method for calculating the closure over sets of functional dependencies and novel features for choosing appropriate constraints. Our evaluation shows that Normalize can process millions of FDs within a few minutes and that the constraint selection techniques support the construction of meaningful relations during normalization.
  @inproceedings{papenbrock2017data, abstract = {Ensuring Boyce-Codd Normal Form (BCNF) is the most popular way to remove redundancy and anomalies from datasets. Normalization to BCNF forces functional dependencies (FDs) into keys and foreign keys, which eliminates duplicate values and makes data constraints explicit. Despite being well researched in theory, converting the schema of an existing dataset into BCNF is still a complex, manual task, especially because the number of functional dependencies is huge and deriving keys and foreign keys is NP-hard. In this paper, we present a novel normalization algorithm called Normalize, which uses discovered functional dependencies to normalize relational datasets into BCNF. Normalize runs entirely data-driven, which means that redundancy is removed only where it can be observed, and it is (semi-)automatic, which means that a user may or may not interfere with the normalization process. The algorithm introduces an efficient method for calculating the closure over sets of functional dependencies and novel features for choosing appropriate constraints. Our evaluation shows that Normalize can process millions of FDs within a few minutes and that the constraint selection techniques support the construction of meaningful relations during normalization.}, author = {Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Extending Database Technology (EDBT)}, journal = {Proceedings of the International Conference on Extending Database Technology (EDBT)}, keywords = {hpi schema data-driven normalization unique_column_combinations profiling isg functional_dependencies hyfd}, pages = {342-353}, title = {Data-driven Schema Normalization}, year = 2017 }
• Repke, T., Loster, M., Krestel, R.: Comparing Features for Ranking Relationships Between Financial Entities Based on Text. Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets. p. 12:1--12:2. ACM, New York, NY, USA (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Evaluating the credibility of a company is an important and complex task for financial experts. When estimating the risk associated with a potential asset, analysts rely on large amounts of data from a variety of different sources, such as newspapers, stock market trends, and bank statements. Finding relevant information, such as relationships between financial entities, in mostly unstructured data is a tedious task and examining all sources by hand quickly becomes infeasible. In this paper, we propose an approach to rank extracted relationships based on text snippets, such that important information can be displayed more prominently. Our experiments with different numerical representations of text have shown, that ensemble of methods performs best on labelled data provided for the FEIII Challenge 2017.
  @inproceedings{Tim2017a, abstract = {Evaluating the credibility of a company is an important and complex task for financial experts. When estimating the risk associated with a potential asset, analysts rely on large amounts of data from a variety of different sources, such as newspapers, stock market trends, and bank statements. Finding relevant information, such as relationships between financial entities, in mostly unstructured data is a tedious task and examining all sources by hand quickly becomes infeasible. In this paper, we propose an approach to rank extracted relationships based on text snippets, such that important information can be displayed more prominently. Our experiments with different numerical representations of text have shown, that ensemble of methods performs best on labelled data provided for the FEIII Challenge 2017.}, address = {New York, NY, USA}, author = {Repke, Tim and Loster, Michael and Krestel, Ralf}, booktitle = {Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets}, keywords = {business_communication web_science isg}, month = {may}, pages = {12:1--12:2}, publisher = {ACM}, series = {DSMM'17}, title = {Comparing Features for Ranking Relationships Between Financial Entities Based on Text}, year = 2017 }
• Kruse, S., Papenbrock, T., Dullweber, C., Finke, M., Hegner, M., Zabel, M., Zöllner, C., Naumann, F.: Fast Approximate Discovery of Inclusion Dependencies. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW). pp. 207-226 (2017).
  [ BibTeX ] [ Download ]
   
  @inproceedings{kruse2017fast, author = {Kruse, Sebastian and Papenbrock, Thorsten and Dullweber, Christian and Finke, Moritz and Hegner, Manuel and Zabel, Martin and Zöllner, Christian and Naumann, Felix}, booktitle = {Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW)}, keywords = {Approximation profiling Inclusion_Dependencies Dependency_Discovery Data_Profiling isg}, pages = {207-226}, title = {Fast Approximate Discovery of Inclusion Dependencies}, year = 2017 }
• Gruetze, T., Krestel, R., Lazaridou, K., Naumann, F.: What was Hillary Clinton doing in Katy, Texas? Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, 3-7 April, 2017. ACM (2017).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  During the last presidential election in the United States of America, Twitter drew a lot of attention. This is because many leading persons and organizations, such as U.S. president Donald J. Trump, showed a strong affection to this medium. In this work we neglect the political contents and opinions shared on Twitter and focus on the question: Can we determine and track the physical location of the presidential candidates based on posts in the Twittersphere?
  @inproceedings{GruetzeClintonTexas2017, abstract = {During the last presidential election in the United States of America, Twitter drew a lot of attention. This is because many leading persons and organizations, such as U.S. president Donald J. Trump, showed a strong affection to this medium. In this work we neglect the political contents and opinions shared on Twitter and focus on the question: Can we determine and track the physical location of the presidential candidates based on posts in the Twittersphere?}, author = {Gruetze, Toni and Krestel, Ralf and Lazaridou, Konstantina and Naumann, Felix}, booktitle = {Proceedings of the 26th International Conference on World Wide Web, {WWW} 2017, Perth, Australia, 3-7 April, 2017}, keywords = {web_science social_media isg}, publisher = {{ACM}}, title = {What was Hillary Clinton doing in Katy, Texas?}, year = 2017 }
• Papenbrock, T., Naumann, F.: A Hybrid Approach for Efficient Unique Column Combination Discovery. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW). pp. 195-204 (2017).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Unique column combinations (UCCs) are groups of attributes in relational datasets that contain no value-entry more than once. Hence, they indicate keys and serve data management tasks, such as schema normalization, data integration, and data cleansing. Because the unique column combinations of a particular dataset are usually unknown, UCC discovery algorithms have been proposed to find them. All previous such discovery algorithms are, however, inapplicable to datasets of typical real-world size, e.g., datasets with more than 50 attributes and a million records. We present the hybrid discovery algorithm HyUCC, which uses the same discovery techniques as the recently proposed functional dependency discovery algorithm HyFD: A hybrid combination of fast approximation techniques and efficient validation techniques. With it, the algorithm discovers all minimal unique column combinations in a given dataset. HyUCC does not only outperform all existing approaches, it also scales to much larger datasets.
  @inproceedings{papenbrock2017hyucc, abstract = {Unique column combinations (UCCs) are groups of attributes in relational datasets that contain no value-entry more than once. Hence, they indicate keys and serve data management tasks, such as schema normalization, data integration, and data cleansing. Because the unique column combinations of a particular dataset are usually unknown, UCC discovery algorithms have been proposed to find them. All previous such discovery algorithms are, however, inapplicable to datasets of typical real-world size, e.g., datasets with more than 50 attributes and a million records. We present the hybrid discovery algorithm HyUCC, which uses the same discovery techniques as the recently proposed functional dependency discovery algorithm HyFD: A hybrid combination of fast approximation techniques and efficient validation techniques. With it, the algorithm discovers all minimal unique column combinations in a given dataset. HyUCC does not only outperform all existing approaches, it also scales to much larger datasets.}, author = {Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW)}, journal = {Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW)}, keywords = {hpi hyucc parallel unique_column_combinations profiling discovery isg}, pages = {195-204}, title = {A Hybrid Approach for Efficient Unique Column Combination Discovery}, year = 2017 }
• Bleifuß, T., Johnson, T., Kalashnikov, D.V., Naumann, F., Shkapenyuk, V., Srivastava, D.: Enabling Change Exploration (Vision). Proceedings of the Fourth International Workshop on Exploratory Search in Databases and the Web (ExploreDB). pp. 1-3 (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Data and metadata suffer many different kinds of change: values are inserted, deleted or updated, entities appear and disappear, properties are added or re-purposed, etc. Explicitly recognizing, exploring, and evaluating such change can alert to changes in data ingestion procedures, can help assess data quality, and can improve the general understanding of the dataset and its behavior over time. We propose a data model-independent framework to formalize such change. Our change-cube enables exploration and discovery of such changes to reveal dataset behavior over time.
  @inproceedings{bleifuss2017change, abstract = {Data and metadata suffer many different kinds of change: values are inserted, deleted or updated, entities appear and disappear, properties are added or re-purposed, etc. Explicitly recognizing, exploring, and evaluating such change can alert to changes in data ingestion procedures, can help assess data quality, and can improve the general understanding of the dataset and its behavior over time. We propose a data model-independent framework to formalize such change. Our change-cube enables exploration and discovery of such changes to reveal dataset behavior over time.}, author = {Bleifuß, Tobias and Johnson, Theodore and Kalashnikov, Dmitri V. and Naumann, Felix and Shkapenyuk, Vladislav and Srivastava, Divesh}, booktitle = {Proceedings of the Fourth International Workshop on Exploratory Search in Databases and the Web (ExploreDB)}, keywords = {isg}, pages = {1-3}, title = {Enabling Change Exploration (Vision)}, year = 2017 }
• Harmouch, H., Naumann, F.: Cardinality Estimation: An Experimental Survey. Proceedings of the VLDB Endowment (PVLDB). pp. 499 - 512 (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Data preparation and data profiling comprise many both basic and complex tasks to analyze a dataset at hand and extract metadata, such as data distributions, key candidates, and functional dependencies. Among the most important types of metadata is the number of distinct values in a column, also known as the zeroth-frequency moment. Cardinality estimation itself has been an active research topic in the past decades due to its many applications. The aim of this paper is to review the literature of cardinality estimation and to present a detailed experimental study of twelve algorithms, scaling far beyond the original experiments. First, we outline and classify approaches to solve the problem of cardinality estimation- we describe their main idea, error guarantees, advantages, and disadvantages. Our experimental survey then compares the performance all twelve cardinality estimation algorithms. We evaluate the algorithms' accuracy, runtime, and memory consumption using synthetic and real-world datasets. Our results show that different algorithms excel in different in categories, and we highlight their trade-offs
  @inproceedings{harmouch2017cardinality, abstract = {Data preparation and data profiling comprise many both basic and complex tasks to analyze a dataset at hand and extract metadata, such as data distributions, key candidates, and functional dependencies. Among the most important types of metadata is the number of distinct values in a column, also known as the zeroth-frequency moment. Cardinality estimation itself has been an active research topic in the past decades due to its many applications. The aim of this paper is to review the literature of cardinality estimation and to present a detailed experimental study of twelve algorithms, scaling far beyond the original experiments. First, we outline and classify approaches to solve the problem of cardinality estimation- we describe their main idea, error guarantees, advantages, and disadvantages. Our experimental survey then compares the performance all twelve cardinality estimation algorithms. We evaluate the algorithms' accuracy, runtime, and memory consumption using synthetic and real-world datasets. Our results show that different algorithms excel in different in categories, and we highlight their trade-offs}, author = {Harmouch, Hazar and Naumann, Felix}, booktitle = {Proceedings of the VLDB Endowment (PVLDB)}, keywords = {hpi profiling distinct_value isg cardinality}, month = 12, number = 4, pages = {499 - 512}, title = {Cardinality Estimation: An Experimental Survey}, volume = 11, year = 2017 }
• Naumann, F., Krestel, R.: Das Fachgebiet „Informationssysteme“ am Hasso-Plattner-Institut. Datenbankspektrum. 17, 69-76 (2017).
  [ BibTeX ] [ URL ]
   
  @article{FG-IS17, author = {Naumann, Felix and Krestel, Ralf}, journal = {Datenbankspektrum}, keywords = {web_science isg}, number = 1, pages = {69-76}, title = {Das Fachgebiet „Informationssysteme“ am Hasso-Plattner-Institut}, volume = 17, year = 2017 }
• Bleifuß, T., Kruse, S., Naumann, F.: Efficient Denial Constraint Discovery with Hydra. Proceedings of the VLDB Endowment (PVLDB). 11, 311-323 (2017).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Denial constraints (DCs) are a generalization of many other integrity constraints (ICs) widely used in databases, such as key constraints, functional dependencies, or order dependencies. Therefore, they can serve as a unified reasoning framework for all of these ICs and express business rules that cannot be expressed by the more restrictive IC types. The process of formulating DCs by hand is difficult, because it requires not only domain expertise but also database knowledge, and due to DCs' inherent complexity, this process is tedious and error-prone. Hence, an automatic DC discovery is highly desirable: we search for all valid denial constraints in a given database instance. However, due to the large search space, the problem of DC discovery is computationally expensive. We propose a new algorithm Hydra, which overcomes the quadratic runtime complexity in the number of tuples of state-of-the-art DC discovery methods. The new algorithm's experimentally determined runtime grows only linearly in the number of tuples. This results in a speedup by orders of magnitude, especially for datasets with a large number of tuples. Hydra can deliver results in a matter of seconds that to date took hours to compute.
  @article{bleifuss2018hydra, abstract = {Denial constraints (DCs) are a generalization of many other integrity constraints (ICs) widely used in databases, such as key constraints, functional dependencies, or order dependencies. Therefore, they can serve as a unified reasoning framework for all of these ICs and express business rules that cannot be expressed by the more restrictive IC types. The process of formulating DCs by hand is difficult, because it requires not only domain expertise but also database knowledge, and due to DCs' inherent complexity, this process is tedious and error-prone. Hence, an automatic DC discovery is highly desirable: we search for all valid denial constraints in a given database instance. However, due to the large search space, the problem of DC discovery is computationally expensive. We propose a new algorithm Hydra, which overcomes the quadratic runtime complexity in the number of tuples of state-of-the-art DC discovery methods. The new algorithm's experimentally determined runtime grows only linearly in the number of tuples. This results in a speedup by orders of magnitude, especially for datasets with a large number of tuples. Hydra can deliver results in a matter of seconds that to date took hours to compute.}, author = {Bleifuß, Tobias and Kruse, Sebastian and Naumann, Felix}, journal = {Proceedings of the VLDB Endowment (PVLDB)}, keywords = {profiling isg}, number = 3, pages = {311-323}, title = {Efficient Denial Constraint Discovery with Hydra}, volume = 11, year = 2017 }
• Giesler, M.J., Keller, B., Repke, T., Leonhart, R., Weis, J., Muckelbauer, R., Rieckmann, N., Müller-Nordhorn, J., Lucius-Hoene, G., Holmberg, C.: Effect of a Website That Presents Patients' Experiences on Self-Efficacy and Patient Competence of Colorectal Cancer Patients: Web-Based Randomized Controlled Trial. J Med Internet Res. 19, e334 (2017).
  [ Abstract ] [ BibTeX ] [ URL ]
   
  Background: Patients often seek other patients' experiences with the disease. The Internet provides a wide range of opportunities to share and learn about other people's health and illness experiences via blogs or patient-initiated online discussion groups. There also exists a range of medical information devices that include experiential patient information. However, there are serious concerns about the use of such experiential information because narratives of others may be powerful and pervasive tools that may hinder informed decision making. The international research network DIPEx (Database of Individual Patients' Experiences) aims to provide scientifically based online information on people's experiences with health and illness to fulfill patients' needs for experiential information, while ensuring that the presented information includes a wide variety of possible experiences. Objective: The aim is to evaluate the colorectal cancer module of the German DIPEx website krankheitserfahrungen.de with regard to self-efficacy for coping with cancer and patient competence. Methods: In 2015, a Web-based randomized controlled trial was conducted using a two-group between-subjects design and repeated measures. The study sample consisted of individuals who had been diagnosed with colorectal cancer within the past 3 years or who had metastasis or recurrent disease. Outcome measures included self-efficacy for coping with cancer and patient competence. Participants were randomly assigned to either an intervention group that had immediate access to the colorectal cancer module for 2 weeks or to a waiting list control group. Outcome criteria were measured at baseline before randomization and at 2 weeks and 6 weeks Results: The study randomized 212 persons. On average, participants were 54 (SD 11.1) years old, 58.8% (124/211) were female, and 73.6% (156/212) had read or heard stories of other patients online before entering the study, thus excluding any influence of the colorectal cancer module on krankheitserfahrungen.de. No intervention effects were found at 2 and 6 weeks after baseline. Conclusions: The results of this study do not support the hypothesis that the website studied may increase self-efficacy for coping with cancer or patient competencies such as self-regulation or managing emotional distress. Possible explanations may involve characteristics of the website itself, its use by participants, or methodological reasons. Future studies aimed at evaluating potential effects of websites providing patient experiences on the basis of methodological principles such as those of DIPEx might profit from extending the range of outcome measures, from including additional measures of website usage behavior and users' motivation, and from expanding concepts, such as patient competency to include items that more directly reflect patients' perceived effects of using such a website. Trial Registration: Clinicaltrials.gov NCT02157454; https://clinicaltrials.gov/ct2/show/NCT02157454 (Archived by WebCite at http://www.webcitation.org/6syrvwXxi)
  @article{info:doi/10.2196/jmir.7639, abstract = {Background: Patients often seek other patients' experiences with the disease. The Internet provides a wide range of opportunities to share and learn about other people's health and illness experiences via blogs or patient-initiated online discussion groups. There also exists a range of medical information devices that include experiential patient information. However, there are serious concerns about the use of such experiential information because narratives of others may be powerful and pervasive tools that may hinder informed decision making. The international research network DIPEx (Database of Individual Patients' Experiences) aims to provide scientifically based online information on people's experiences with health and illness to fulfill patients' needs for experiential information, while ensuring that the presented information includes a wide variety of possible experiences. Objective: The aim is to evaluate the colorectal cancer module of the German DIPEx website krankheitserfahrungen.de with regard to self-efficacy for coping with cancer and patient competence. Methods: In 2015, a Web-based randomized controlled trial was conducted using a two-group between-subjects design and repeated measures. The study sample consisted of individuals who had been diagnosed with colorectal cancer within the past 3 years or who had metastasis or recurrent disease. Outcome measures included self-efficacy for coping with cancer and patient competence. Participants were randomly assigned to either an intervention group that had immediate access to the colorectal cancer module for 2 weeks or to a waiting list control group. Outcome criteria were measured at baseline before randomization and at 2 weeks and 6 weeks Results: The study randomized 212 persons. On average, participants were 54 (SD 11.1) years old, 58.8\% (124/211) were female, and 73.6\% (156/212) had read or heard stories of other patients online before entering the study, thus excluding any influence of the colorectal cancer module on krankheitserfahrungen.de. No intervention effects were found at 2 and 6 weeks after baseline. Conclusions: The results of this study do not support the hypothesis that the website studied may increase self-efficacy for coping with cancer or patient competencies such as self-regulation or managing emotional distress. Possible explanations may involve characteristics of the website itself, its use by participants, or methodological reasons. Future studies aimed at evaluating potential effects of websites providing patient experiences on the basis of methodological principles such as those of DIPEx might profit from extending the range of outcome measures, from including additional measures of website usage behavior and users' motivation, and from expanding concepts, such as patient competency to include items that more directly reflect patients' perceived effects of using such a website. Trial Registration: Clinicaltrials.gov NCT02157454; https://clinicaltrials.gov/ct2/show/NCT02157454 (Archived by WebCite at http://www.webcitation.org/6syrvwXxi)}, author = {Giesler, M. Jürgen and Keller, Bettina and Repke, Tim and Leonhart, Rainer and Weis, Joachim and Muckelbauer, Rebecca and Rieckmann, Nina and Müller-Nordhorn, Jacqueline and Lucius-Hoene, Gabriele and Holmberg, Christine}, journal = {J Med Internet Res}, keywords = {trepke}, month = {oct}, number = 10, pages = {e334}, title = {Effect of a Website That Presents Patients' Experiences on Self-Efficacy and Patient Competence of Colorectal Cancer Patients: Web-Based Randomized Controlled Trial}, volume = 19, year = 2017 }
• Tschirschnitz, F., Papenbrock, T., Naumann, F.: Detecting Inclusion Dependencies on Very Many Tables. ACM Transactions on Database Systems (TODS). 42, 18:1-18:29 (2017).
  [ BibTeX ] [ URL ] [ Download ]
   
  @article{tschirschnitz2017detecting, address = {New York, NY, USA}, author = {Tschirschnitz, Fabian and Papenbrock, Thorsten and Naumann, Felix}, journal = {ACM Transactions on Database Systems (TODS)}, keywords = {foreign_key hpi inclusion_dependency profiling discovery isg}, month = {jul}, number = 3, pages = {18:1-18:29}, publisher = {ACM}, title = {Detecting Inclusion Dependencies on Very Many Tables}, volume = 42, year = 2017 }
• Heller, D., Krestel, R., Ohler, U., Vingron, M., Marsico, A.: ssHMM: Extracting Intuitive Sequence-Structure Motifs from High-Throughput RNA-Binding Protein Data. Nucleic Acid Research. 45, 11004--11018 (2017).
  [ Abstract ] [ BibTeX ]
   
  RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM’s model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image.
  @article{krestel-nar17, abstract = {RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM’s model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image.}, author = {Heller, David and Krestel, Ralf and Ohler, Uwe and Vingron, Martin and Marsico, Annalisa}, journal = {Nucleic Acid Research}, keywords = {bio_inf web_science isg}, number = 19, pages = {11004--11018}, publisher = {Oxford Academic}, title = {ssHMM: Extracting Intuitive Sequence-Structure Motifs from High-Throughput RNA-Binding Protein Data}, volume = 45, year = 2017 }

2016

• Krestel, R., Mottin, D., Müller, E. eds: Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Potsdam, Germany, September 12-14, 2016. CEUR-WS.org (2016).
  [ BibTeX ]
   
  @proceedings{lwda16proc, editor = {Krestel, Ralf and Mottin, Davide and Müller, Emmanuel}, keywords = {web_science isg}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", Potsdam, Germany, September 12-14, 2016}, volume = 1670, year = 2016 }
• Samiei, A., Koumarelas, I., Loster, M., Naumann, F.: Combination of Rule-based and Textual Similarity Approaches to Match Financial Entities. Proceedings of the Second International Workshop on Data Science for Macro-Modeling. ACM (2016).
  [ Abstract ] [ BibTeX ] [ URL ]
   
  Record linkage is a well studied problem with many years of publication history. Nevertheless, there are many challenges remaining to be addressed, such as the topic addressed by FEIII Challenge 2016. Matching financial entities (FEs) is important for many private and governmental organizations. In this paper we describe the problem of matching such FEs across three datasets: FFIEC, LEI and SEC.
  @inproceedings{Ahmad2016b, abstract = {Record linkage is a well studied problem with many years of publication history. Nevertheless, there are many challenges remaining to be addressed, such as the topic addressed by FEIII Challenge 2016. Matching financial entities (FEs) is important for many private and governmental organizations. In this paper we describe the problem of matching such FEs across three datasets: FFIEC, LEI and SEC.}, author = {Samiei, Ahmad and Koumarelas, Ioannis and Loster, Michael and Naumann, Felix}, journal = {Proceedings of the Second International Workshop on Data Science for Macro-Modeling}, keywords = {isg}, publisher = {ACM}, series = {DSMM'16}, title = {Combination of Rule-based and Textual Similarity Approaches to Match Financial Entities}, year = 2016 }
• Park, J., Blume-Kohout, M., Krestel, R., Nalisnick, E., Smyth, P.: Analyzing NIH Funding Patterns over Time with Statistical Text Analysis. Scholarly Big Data: AI Perspectives, Challenges, and Ideas (SBD 2016) Workshop at AAAI 2016. AAAI (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  In the past few years various government funding organizations such as the U.S. National Institutes of Health and the U.S. National Science Foundation have provided access to large publicly-available on-line databases documenting the grants that they have funded over the past few decades. These databases provide an excellent opportunity for the application of statistical text analysis techniques to infer useful quantitative information about how funding patterns have changed over time. In this paper we analyze data from the National Cancer Institute (part of National Institutes of Health) and show how text classification techniques provide a useful starting point for analyzing how funding for cancer research has evolved over the past 20 years in the United States.
  @inproceedings{sbd16, abstract = {In the past few years various government funding organizations such as the U.S. National Institutes of Health and the U.S. National Science Foundation have provided access to large publicly-available on-line databases documenting the grants that they have funded over the past few decades. These databases provide an excellent opportunity for the application of statistical text analysis techniques to infer useful quantitative information about how funding patterns have changed over time. In this paper we analyze data from the National Cancer Institute (part of National Institutes of Health) and show how text classification techniques provide a useful starting point for analyzing how funding for cancer research has evolved over the past 20 years in the United States.}, author = {Park, Jihyun and Blume-Kohout, Margaret and Krestel, Ralf and Nalisnick, Eric and Smyth, Padhraic}, booktitle = {Scholarly Big Data: AI Perspectives, Challenges, and Ideas (SBD 2016) Workshop at AAAI 2016}, keywords = {bio_inf topic_models web_science cross_collection isg}, month = 2, publisher = {AAAI}, title = {Analyzing NIH Funding Patterns over Time with Statistical Text Analysis}, year = 2016 }
• Jenders, M., Krestel, R., Naumann, F.: Which Answer is Best? Predicting Accepted Answers in MOOC Forums. Proceedings of the 25th International Conference Companion on World Wide Web. pp. 679-684. International World Wide Web Conferences Steering Committee (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a user posting a question can mark the answer they are most satisfied with. In this paper, we present a machine learning model that predicts this accepted answer to a forum question using historical forum data.
  @inproceedings{Jenders2016, abstract = {Massive Open Online Courses (MOOCs) have grown in reach and importance over the last few years, enabling a vast userbase to enroll in online courses. Besides watching videos, user participate in discussion forums to further their understanding of the course material. As in other community-based question-answering communities, in many MOOC forums a user posting a question can mark the answer they are most satisfied with. In this paper, we present a machine learning model that predicts this accepted answer to a forum question using historical forum data.}, author = {Jenders, Maximilian and Krestel, Ralf and Naumann, Felix}, booktitle = {Proceedings of the 25th International Conference Companion on World Wide Web}, keywords = {web_science social_media isg}, month = 4, organization = {International World Wide Web Conferences Steering Committee}, pages = {679-684}, title = {Which Answer is Best? Predicting Accepted Answers in MOOC Forums}, year = 2016 }
• Gruetze, T., Krestel, R., Naumann, F.: Topic Shifts in StackOverflow: Ask it like Socrates. Lecture Notes in Computer Science. p. 213--221. Springer (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.
  @inproceedings{GruetzeStackOverflow2016, abstract = {Community based question-and-answer (Q&A) sites rely on well posed and appropriately tagged questions. However, most platforms have only limited capabilities to support their users in finding the right tags. In this paper, we propose a temporal recommendation model to support users in tagging new questions and thus improve their acceptance in the community. To underline the necessity of temporal awareness of such a model, we first investigate the changes in tag usage and show different types of collective attention in StackOverflow, a community-driven Q&A website for computer programming topics. Furthermore, we examine the changes over time in the correlation between question terms and topics. Our results show that temporal awareness is indeed important for recommending tags in Q&A communities.}, author = {Gruetze, Toni and Krestel, Ralf and Naumann, Felix}, booktitle = {Proceedings of the 21st International Conference on Applications of Natual Language to Information Systems (NLDB)}, journal = {Lecture Notes in Computer Science}, keywords = {web_science social_media isg}, month = 6, pages = {213--221}, publisher = {Springer}, title = {Topic Shifts in StackOverflow: Ask it like Socrates}, volume = 9612, year = 2016 }
• Godde, C., Lazaridou, K., Krestel, R.: Classification of German Newspaper Comments. Proceedings of the Conference Lernen, Wissen, Daten, Analysen. pp. 299-310. CEUR-WS.org (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Online news has gradually become an inherent part of many people’s every day life, with the media enabling a social and interactive consumption of news as well. Readers openly express their perspectives and emotions for a current event by commenting news articles. They also form online communities and interact with each other by replying to other users’ comments. Due to their active and significant role in the diffusion of information, automatically gaining insights of these comments’ content is an interesting task. We are especially interested in finding systematic differences among the user comments from different newspapers. To this end, we propose the following classification task: Given a news comment thread of a particular article, identify the newspaper it comes from. Our corpus consists of six well-known German newspapers and their comments. We propose two experimental settings using SVM classifiers build on comment- and article-based features. We achieve precision of up to 90% for individual newspapers.
  @inproceedings{lwda16, abstract = {Online news has gradually become an inherent part of many people’s every day life, with the media enabling a social and interactive consumption of news as well. Readers openly express their perspectives and emotions for a current event by commenting news articles. They also form online communities and interact with each other by replying to other users’ comments. Due to their active and significant role in the diffusion of information, automatically gaining insights of these comments’ content is an interesting task. We are especially interested in finding systematic differences among the user comments from different newspapers. To this end, we propose the following classification task: Given a news comment thread of a particular article, identify the newspaper it comes from. Our corpus consists of six well-known German newspapers and their comments. We propose two experimental settings using SVM classifiers build on comment- and article-based features. We achieve precision of up to 90% for individual newspapers.}, author = {Godde, Christian and Lazaridou, Konstantina and Krestel, Ralf}, booktitle = {Proceedings of the Conference Lernen, Wissen, Daten, Analysen}, keywords = {comments_analysis web_science isg}, month = 9, pages = {299-310}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Classification of German Newspaper Comments}, volume = 1670, year = 2016 }
• Samiei, A., Naumann, F.: Cluster-based Sorted Neighborhood for Efficient Duplicate Detection. International Conference on Data Mining Workshops (ICDMW) (2016).
  [ Abstract ] [ BibTeX ]
   
  Duplicate detection intends to find multiple and syntactically different representations of the same real-world entities in a dataset. The naive way of duplicate detection entails a quadratic number of pair-wise record comparisons to identify the duplicates. This number of comparisons might take hours even for an average sized dataset. As today's databases grow very fast, different candidate-selection methods, such as sorted neighborhood, blocking, canopy clustering and their variations, address this problem by shrinking the comparison space. The volume and velocity of data-change require ever faster and more flexible methods of duplicate detection. In particular, they need dynamic indices that can be updated efficiently as new data arrives. We present a novel approach, which combines the idea of cluster-based methods with the well-known sorted neighborhood method. It carefully filters out irrelevant candidate pairs, which are less likely to yield duplicates, by pre-clustering records based not only on their proximity after sorting, but also on their similarity in selected attributes. An empirical evaluation on synthetic and real-world datasets shows that our approach improves the overall runtime over existing approaches while maintaining comparable result quality. Meanwhile, it uses dynamic indices, that in turns make it useful for deduplicating streaming data.
  @inproceedings{Ahmad2016a, abstract = {Duplicate detection intends to find multiple and syntactically different representations of the same real-world entities in a dataset. The naive way of duplicate detection entails a quadratic number of pair-wise record comparisons to identify the duplicates. This number of comparisons might take hours even for an average sized dataset. As today's databases grow very fast, different candidate-selection methods, such as sorted neighborhood, blocking, canopy clustering and their variations, address this problem by shrinking the comparison space. The volume and velocity of data-change require ever faster and more flexible methods of duplicate detection. In particular, they need dynamic indices that can be updated efficiently as new data arrives. We present a novel approach, which combines the idea of cluster-based methods with the well-known sorted neighborhood method. It carefully filters out irrelevant candidate pairs, which are less likely to yield duplicates, by pre-clustering records based not only on their proximity after sorting, but also on their similarity in selected attributes. An empirical evaluation on synthetic and real-world datasets shows that our approach improves the overall runtime over existing approaches while maintaining comparable result quality. Meanwhile, it uses dynamic indices, that in turns make it useful for deduplicating streaming data.}, author = {Samiei, Ahmad and Naumann, Felix}, journal = {International Conference on Data Mining Workshops (ICDMW)}, keywords = {isg}, title = {Cluster-based Sorted Neighborhood for Efficient Duplicate Detection}, year = 2016 }
• Ziawasch Abedjan, L.G., Naumann, F.: Data Profiling (tutorial). International Conference on Data Engineering (ICDE) (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  One of the crucial requirements before consuming datasets for any application is to understand the dataset at hand and its metadata. The process of metadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of structural information and statistics of a dataset using dedicated profiling tools. In this tutorial, we highlight the importance of data profiling as part of any data-related use-case, and discuss the area of data profiling by classifying data profiling tasks and reviewing the state-of-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and profiling algorithms for dynamic data and streams. We conclude with directions for future research in the area of data profiling. This tutorial is based on our survey on profiling relational data [1].
  @inproceedings{Abedjan16, abstract = {One of the crucial requirements before consuming datasets for any application is to understand the dataset at hand and its metadata. The process of metadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of structural information and statistics of a dataset using dedicated profiling tools. In this tutorial, we highlight the importance of data profiling as part of any data-related use-case, and discuss the area of data profiling by classifying data profiling tasks and reviewing the state-of-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and profiling algorithms for dynamic data and streams. We conclude with directions for future research in the area of data profiling. This tutorial is based on our survey on profiling relational data [1].}, author = {Ziawasch Abedjan, Lukasz Golab and Naumann, Felix}, booktitle = {International Conference on Data Engineering (ICDE)}, keywords = {isg}, title = {Data Profiling (tutorial)}, year = 2016 }
• Ehrlich, J., Roick, M., Schulze, L., Zwiener, J., Papenbrock, T., Naumann, F.: Holistic Data Profiling: Simultaneous Discovery of Various Metadata. Proceedings of the International Conference on Extending Database Technology (EDBT). pp. 305-316. OpenProceedings.org (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Data profiling is the discipline of examining an unknown dataset for its structure and statistical information. It is a preprocessing step in a wide range of applications, such as data integration, data cleansing, or query optimization. For this reason, many algorithms have been proposed for the discovery of different kinds of metadata. When analyzing a dataset, these profiling algorithms are often applied in sequence, but they do not support one another, for instance, by sharing I/O cost or pruning information. We present the holistic algorithm MUDS, which jointly discovers the three most important metadata: inclusion dependencies, unique column combinations, and functional dependencies. By sharing I/O cost and data structures across the different discovery tasks, MUDS can clearly increase the efficiency of traditional sequential data profiling. The algorithm also introduces novel inter-task pruning rules that build upon different types of metadata, e.g., unique column combinations to infer functional dependencies. We evaluate MUDS in detail and compare it against the sequential execution of state-of-the-art algorithms. A comprehensive evaluation shows that our holistic algorithm outperforms the baseline by up to factor 48 on datasets with favorable pruning conditions.
  @inproceedings{ehrlich2016holistic, abstract = {Data profiling is the discipline of examining an unknown dataset for its structure and statistical information. It is a preprocessing step in a wide range of applications, such as data integration, data cleansing, or query optimization. For this reason, many algorithms have been proposed for the discovery of different kinds of metadata. When analyzing a dataset, these profiling algorithms are often applied in sequence, but they do not support one another, for instance, by sharing I/O cost or pruning information. We present the holistic algorithm MUDS, which jointly discovers the three most important metadata: inclusion dependencies, unique column combinations, and functional dependencies. By sharing I/O cost and data structures across the different discovery tasks, MUDS can clearly increase the efficiency of traditional sequential data profiling. The algorithm also introduces novel inter-task pruning rules that build upon different types of metadata, e.g., unique column combinations to infer functional dependencies. We evaluate MUDS in detail and compare it against the sequential execution of state-of-the-art algorithms. A comprehensive evaluation shows that our holistic algorithm outperforms the baseline by up to factor 48 on datasets with favorable pruning conditions.}, author = {Ehrlich, Jens and Roick, Mandy and Schulze, Lukas and Zwiener, Jakob and Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Extending Database Technology (EDBT)}, keywords = {inclusion_dependencies unique_column_combinations holistic discovery isg functional_dependencies}, pages = {305-316}, publisher = {OpenProceedings.org}, title = {Holistic Data Profiling: Simultaneous Discovery of Various Metadata}, year = 2016 }
• Bleifuß, T., Bülow, S., Frohnhofen, J., Risch, J., Wiese, G., Kruse, S., Papenbrock, T., Naumann, F.: Approximate Discovery of Functional Dependencies for Large Datasets. Proceedings of the International Conference on Information and Knowledge Management (CIKM). pp. 1803-1812. ACM, New York, NY, USA (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Functional dependencies (FDs) are an important prerequisite for various data management tasks, such as schema normalization, query optimization, and data cleansing. However, automatic FD discovery entails an exponentially growing search and solution space, so that even today’s fastest FD discovery algorithms are limited to small datasets only, due to long runtimes and high memory consumptions. To overcome this situation, we propose an approximate discovery strategy that sacrifices possibly little result correctness in return for large performance improvements. In particular, we introduce AID-FD, an algorithm that approximately discovers FDs within runtimes up to orders of magnitude faster than state-of-the-art FD discovery algorithms. We evaluate and compare our performance results with a focus on scalability in runtime and memory, and with measures for completeness, correctness, and minimality.
  @inproceedings{bleifuss2016approximate, abstract = {Functional dependencies (FDs) are an important prerequisite for various data management tasks, such as schema normalization, query optimization, and data cleansing. However, automatic FD discovery entails an exponentially growing search and solution space, so that even today’s fastest FD discovery algorithms are limited to small datasets only, due to long runtimes and high memory consumptions. To overcome this situation, we propose an approximate discovery strategy that sacrifices possibly little result correctness in return for large performance improvements. In particular, we introduce AID-FD, an algorithm that approximately discovers FDs within runtimes up to orders of magnitude faster than state-of-the-art FD discovery algorithms. We evaluate and compare our performance results with a focus on scalability in runtime and memory, and with measures for completeness, correctness, and minimality.}, address = {New York, NY, USA}, author = {Bleifuß, Tobias and Bülow, Susanne and Frohnhofen, Johannes and Risch, Julian and Wiese, Georg and Kruse, Sebastian and Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM)}, keywords = {approximate hpi profiling discovery isg functional_dependencies}, pages = {1803-1812}, publisher = {ACM}, series = {CIKM '16}, title = {Approximate Discovery of Functional Dependencies for Large Datasets}, year = 2016 }
• Grundke, M., Jasper, J., Perchyk, M., Sachse, J.P., Krestel, R., Neves, M.: TextAI: Enhancing TextAE with Intelligent Annotation Support. Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine (SMBM 2016). pp. 80-84. CEUR-WS.org (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  We present TextAI, an extension to the annotation tool TextAE, that adds support for named-entity recognition and automated relation extraction based on machine learning techniques. Our learning approach is domain-independent and increases the quality of the detected relations with each added training document. We further aim at accelerating and facilitating the manual curation process for natural language documents by supporting simultaneous annotation by multiple users.
  @inproceedings{smbm16, abstract = {We present TextAI, an extension to the annotation tool TextAE, that adds support for named-entity recognition and automated relation extraction based on machine learning techniques. Our learning approach is domain-independent and increases the quality of the detected relations with each added training document. We further aim at accelerating and facilitating the manual curation process for natural language documents by supporting simultaneous annotation by multiple users.}, author = {Grundke, Maximilian and Jasper, Johannes and Perchyk, Mariya and Sachse, Jan Philipp and Krestel, Ralf and Neves, Mariana}, booktitle = {Proceedings of the 7th International Symposium on Semantic Mining in Biomedicine (SMBM 2016)}, keywords = {bio_inf web_science isg}, month = 8, pages = {80-84}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {TextAI: Enhancing TextAE with Intelligent Annotation Support}, volume = 1650, year = 2016 }
• Papenbrock, T., Naumann, F.: A Hybrid Approach to Functional Dependency Discovery. Proceedings of the International Conference on Management of Data (SIGMOD). pp. 821-833. ACM, New York, NY, USA (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Functional dependencies are structural metadata that can be used for schema normalization, data integration, data cleansing, and many other data management tasks. Despite their importance, the functional dependencies of a specific dataset are usually unknown and almost impossible to discover manually. For this reason, database research has proposed various algorithms for functional dependency discovery. None, however, are able to process datasets of typical real-world size, e.g., datasets with more than 50 attributes and a million records. We present a hybrid discovery algorithm called HyFD, which combines fast approximation techniques with efficient validation techniques in order to find all minimal functional dependencies in a given dataset. While operating on compact data structures, HyFD not only outperforms all existing approaches, it also scales to much larger datasets.
  @inproceedings{papenbrock2016hyfd, abstract = {Functional dependencies are structural metadata that can be used for schema normalization, data integration, data cleansing, and many other data management tasks. Despite their importance, the functional dependencies of a specific dataset are usually unknown and almost impossible to discover manually. For this reason, database research has proposed various algorithms for functional dependency discovery. None, however, are able to process datasets of typical real-world size, e.g., datasets with more than 50 attributes and a million records. We present a hybrid discovery algorithm called HyFD, which combines fast approximation techniques with efficient validation techniques in order to find all minimal functional dependencies in a given dataset. While operating on compact data structures, HyFD not only outperforms all existing approaches, it also scales to much larger datasets.}, address = {New York, NY, USA}, author = {Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Management of Data (SIGMOD)}, journal = {Proceedings of the International Conference on Management of Data (SIGMOD)}, keywords = {hpi metadata hybrid parallel profiling discovery isg functional_dependencies hyfd}, pages = {821-833}, publisher = {ACM}, series = {SIGMOD '16}, title = {A Hybrid Approach to Functional Dependency Discovery}, year = 2016 }
• Agrawal, D., Ba, L., Berti-Equille, L., Chawla, S., Elmagarmid, A., Hammady, H., Idris, Y., Kaoudi, Z., Khayyat, Z., Kruse, S., Ouzzani, M., Papotti, P., Quiané-Ruiz, J.-A., Tang, N., Zaki, M.J.: Rheem: Enabling Multi-Platform Task Execution (demo). Proceedings of the ACM SIGMOD conference (SIGMOD) (2016).
  [ BibTeX ] [ Download ]
   
  @inproceedings{agrawal2016rheem, author = {Agrawal, Divy and Ba, Lamine and Berti-Equille, Laure and Chawla, Sanjay and Elmagarmid, Ahmed and Hammady, Hossam and Idris, Yasser and Kaoudi, Zoi and Khayyat, Zuhair and Kruse, Sebastian and Ouzzani, Mourad and Papotti, Paolo and Quiané-Ruiz, Jorge-Arnulfo and Tang, Nan and Zaki, Mohammed J.}, booktitle = {Proceedings of the ACM SIGMOD conference (SIGMOD)}, keywords = {isg}, title = {Rheem: Enabling Multi-Platform Task Execution (demo)}, year = 2016 }
• Kruse, S., Jentzsch, A., Papenbrock, T., Kaoudi, Z., Quiane-Ruiz, J.-A., Naumann, F.: RDFind: Scalable Conditional Inclusion Dependency Discovery in RDF Datasets. Proceedings of the International Conference on Management of Data (SIGMOD). pp. 953-967. ACM, New York, NY, USA (2016).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Inclusion dependencies (inds) form an important integrity constraint on relational databases, supporting data management tasks, such as join path discovery and query optimization. Conditional inclusion dependencies (cinds), which define including and included data in terms of conditions, allow to transfer these capabilities to rdf data. However, cind discovery is computationally much more complex than ind discovery and the number of cinds even on small rdf datasets is intractable. To cope with both problems, we first introduce the notion of pertinent cinds with an adjustable relevance criterion to filter and rank cinds based on their extent and implications among each other. Second, we present RDFind, a distributed system to efficiently discover all pertinent cinds in rdf data. RDFind employs a lazy pruning strategy to drastically reduce the cind search space. Also, its exhaustive parallelization strategy and robust data structures make it highly scalable. In our experimental evaluation, we show that RDFind is up to 419 times faster than the state-of-the-art, while considering a more general class of cinds. Furthermore, it is capable of processing a very large dataset of billions of triples, which was entirely infeasible before.
  @inproceedings{kruse2016rdfind, abstract = {Inclusion dependencies (inds) form an important integrity constraint on relational databases, supporting data management tasks, such as join path discovery and query optimization. Conditional inclusion dependencies (cinds), which define including and included data in terms of conditions, allow to transfer these capabilities to rdf data. However, cind discovery is computationally much more complex than ind discovery and the number of cinds even on small rdf datasets is intractable. To cope with both problems, we first introduce the notion of pertinent cinds with an adjustable relevance criterion to filter and rank cinds based on their extent and implications among each other. Second, we present RDFind, a distributed system to efficiently discover all pertinent cinds in rdf data. RDFind employs a lazy pruning strategy to drastically reduce the cind search space. Also, its exhaustive parallelization strategy and robust data structures make it highly scalable. In our experimental evaluation, we show that RDFind is up to 419 times faster than the state-of-the-art, while considering a more general class of cinds. Furthermore, it is capable of processing a very large dataset of billions of triples, which was entirely infeasible before.}, address = {New York, NY, USA}, author = {Kruse, Sebastian and Jentzsch, Anja and Papenbrock, Thorsten and Kaoudi, Zoi and Quiane-Ruiz, Jorge-Arnulfo and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Management of Data (SIGMOD)}, keywords = {hpi rdfind inclusion_dependencies parallel profiling isg}, pages = {953-967}, publisher = {ACM}, series = {SIGMOD '16}, title = {RDFind: Scalable Conditional Inclusion Dependency Discovery in RDF Datasets}, year = 2016 }
• Lazaridou, K., Krestel, R.: Identifying Political Bias in News Articles. International Conference on Theory and Practice of Digital Libraries. IEEE Technical Committee on Digital Libraries (2016).
  [ Abstract ] [ BibTeX ] [ URL ]
   
  Individuals' political leaning, such as journalists', politicians' etc. often shapes the public opinion over several issues. In the case of online journalism, due to the numerous ongoing events, newspapers have to choose which stories to cover, emphasize on and possibly express their opinion about. These choices depict their profile and could reveal a potential bias towards a certain perspective or political position. Likewise, politicians' choice of language and the issues they broach are an indication of their beliefs and political orientation. Given the amount of user-generated text content online, such as news articles, blog posts, politician statements etc., automatically analyzing this information becomes increasingly interesting, in order to understand what people stand for and how they influence the general public. In this PhD thesis, we analyze UK news corpora along with parliament speeches in order to identify potential political media bias. We currently examine the politicians' mentions and their quotes in news articles and how this referencing pattern evolves in time.
  @incollection{Lazaridou2016a, abstract = {Individuals' political leaning, such as journalists', politicians' etc. often shapes the public opinion over several issues. In the case of online journalism, due to the numerous ongoing events, newspapers have to choose which stories to cover, emphasize on and possibly express their opinion about. These choices depict their profile and could reveal a potential bias towards a certain perspective or political position. Likewise, politicians' choice of language and the issues they broach are an indication of their beliefs and political orientation. Given the amount of user-generated text content online, such as news articles, blog posts, politician statements etc., automatically analyzing this information becomes increasingly interesting, in order to understand what people stand for and how they influence the general public. In this PhD thesis, we analyze UK news corpora along with parliament speeches in order to identify potential political media bias. We currently examine the politicians' mentions and their quotes in news articles and how this referencing pattern evolves in time.}, author = {Lazaridou, Konstantina and Krestel, Ralf}, journal = {International Conference on Theory and Practice of Digital Libraries}, keywords = {news_analysis web_science isg}, month = 11, note = {TPDL Doctoral Consortium}, number = 2, publisher = {IEEE Technical Committee on Digital Libraries}, series = {Bulletin}, title = {Identifying Political Bias in News Articles}, volume = 12, year = 2016 }
• Kruse, S., Papenbrock, T., Harmouch, H., Naumann, F.: Data Anamnesis: Admitting Raw Data into an Organization. IEEE Data Engineering Bulletin. 39, 8-20 (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Today’s internet offers a plethora of openly available datasets, bearing great potential for novel applications and research. Likewise, rich datasets slumber within organizations. However, all too often those datasets are available only as raw dumps and lack proper documentation or even a schema. Data anamnesis is the first step of any effort to work with such datasets: It determines fundamental properties regarding the datasets’ content, structure, and quality to assess their utility and to put them to use appropriately. Detecting such properties is a key concern of the research area of data profiling, which has developed several viable instruments, such as data type recognition and foreign key discovery. In this article, we perform an anamnesis of the MusicBrainz dataset, an openly available and com- plex discographic database. In particular, we employ data profiling methods to create data summaries and then further analyze those summaries to reverse-engineer the database schema, to understand the data semantics, and to point out tangible schema quality issues. We propose two bottom-up schema quality dimensions, namely conciseness and normality, that measure the fit of the schema with its data, in contrast to a top-down approach that compares a schema with its application requirements.
  @article{kruse2016data, abstract = {Today’s internet offers a plethora of openly available datasets, bearing great potential for novel applications and research. Likewise, rich datasets slumber within organizations. However, all too often those datasets are available only as raw dumps and lack proper documentation or even a schema. Data anamnesis is the first step of any effort to work with such datasets: It determines fundamental properties regarding the datasets’ content, structure, and quality to assess their utility and to put them to use appropriately. Detecting such properties is a key concern of the research area of data profiling, which has developed several viable instruments, such as data type recognition and foreign key discovery. In this article, we perform an anamnesis of the MusicBrainz dataset, an openly available and com- plex discographic database. In particular, we employ data profiling methods to create data summaries and then further analyze those summaries to reverse-engineer the database schema, to understand the data semantics, and to point out tangible schema quality issues. We propose two bottom-up schema quality dimensions, namely conciseness and normality, that measure the fit of the schema with its data, in contrast to a top-down approach that compares a schema with its application requirements.}, author = {Kruse, Sebastian and Papenbrock, Thorsten and Harmouch, Hazar and Naumann, Felix}, journal = {IEEE Data Engineering Bulletin}, keywords = {data_profiling profiling isg data_anamnesis schema_discovery}, month = 6, number = 2, pages = {8-20}, title = {Data Anamnesis: Admitting Raw Data into an Organization}, volume = 39, year = 2016 }
• Gruetze, T., Kasneci, G., Zuo, Z., Naumann, F.: CohEEL: Coherent and Efficient Named Entity Linking through Random Walks. Web Semantics: Science, Services and Agents on the World Wide Web. 37, 75--89 (2016).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  In recent years, the ever-growing amount of documents on the Web as well as in digital libraries led to a considerable increase of valuable textual information about entities. Harvesting entity knowledge from these large text collections is a major challenge. It requires the linkage of textual mentions within the documents with their real-world entities. This process is called entity linking. Solutions to this entity linking problem have typically aimed at balancing the rate of linking correctness (precision) and the linking coverage rate (recall). While entity links in texts could be used to improve various Information Retrieval tasks, such as text summarization, document classification, or topic-based clustering, the linking precision is the decisive factor. For example, for topic-based clustering a method that produces mostly correct links would be more desirable than a high-coverage method that leads to more but also more uncertain clusters. We propose an efficient linking method that uses a random walk strategy to combine a precision-oriented and a recall-oriented classifier in such a way that a high precision is maintained, while recall is elevated to the maximum possible level without affecting precision. An evaluation on three datasets with distinct characteristics demonstrates that our approach outperforms seminal work in the area and shows higher precision and time performance than the most closely related state-of-the-art methods.
  @article{Gruetze2016, abstract = {In recent years, the ever-growing amount of documents on the Web as well as in digital libraries led to a considerable increase of valuable textual information about entities. Harvesting entity knowledge from these large text collections is a major challenge. It requires the linkage of textual mentions within the documents with their real-world entities. This process is called entity linking. Solutions to this entity linking problem have typically aimed at balancing the rate of linking correctness (precision) and the linking coverage rate (recall). While entity links in texts could be used to improve various Information Retrieval tasks, such as text summarization, document classification, or topic-based clustering, the linking precision is the decisive factor. For example, for topic-based clustering a method that produces mostly correct links would be more desirable than a high-coverage method that leads to more but also more uncertain clusters. We propose an efficient linking method that uses a random walk strategy to combine a precision-oriented and a recall-oriented classifier in such a way that a high precision is maintained, while recall is elevated to the maximum possible level without affecting precision. An evaluation on three datasets with distinct characteristics demonstrates that our approach outperforms seminal work in the area and shows higher precision and time performance than the most closely related state-of-the-art methods.}, author = {Gruetze, Toni and Kasneci, Gjergji and Zuo, Zhe and Naumann, Felix}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, keywords = {Machine_Learning Entity_Linking isg Random_Walk Named_Entity_Disambiguation}, month = 3, number = {C}, pages = {75--89}, publisher = {Elsevier B.V.}, title = {CohEEL: Coherent and Efficient Named Entity Linking through Random Walks}, volume = 37, year = 2016 }
• Naumann, F., Krestel, R.: The Information Systems Group at HPI. SIGMOD Record. (2016).
  [ BibTeX ] [ Download ]
   
  @article{Naumann16, author = {Naumann, Felix and Krestel, Ralf}, journal = {SIGMOD Record}, keywords = {web_science isg}, title = {The Information Systems Group at HPI}, year = 2016 }
• Langer, P., Naumann, F.: Efficient Order Dependency Discovery. VLDB Journal. 25, 223-241 (2016).
  [ Abstract ] [ BibTeX ]
   
  Order dependencies (ODs) describe a relationship of order between lists of attributes in a relational table. ODs can help to understand the semantics of datasets and the applications producing them. They have applications in the field of query optimization by suggesting query rewrites. Also, the existence of an OD in a table can provide hints on which integrity constraints are valid for the domain of the data at hand. This work is the first to describe the discovery problem for order dependencies in a principled manner by characterizing the search space, developing and proving pruning rules, and presenting the algorithm Order, which finds all order dependencies in a given table. Order traverses the lattice of permutations of attributes in a level-wise bottom-up manner. In a comprehensive evaluation we show that it is efficient even for various large datasets. <p> Szlichta et al. propose a more efficient algorithm to discover order dependencies. In their paper they also point out flaws of our proposal:<br> Jaroslaw Szlichta, Parke Godfrey, Lukasz Golab, Mehdi Kargar, Divesh Srivastava: <a href="http://www.vldb.org/pvldb/vol10/p721-szlichta.pdf">Effective and Complete Discovery of Order Dependencies via Set-based Axiomatization</a>, in PVLDB 10(7), p. 721 - 732, 2017.
  @article{Langer16, abstract = {Order dependencies (ODs) describe a relationship of order between lists of attributes in a relational table. ODs can help to understand the semantics of datasets and the applications producing them. They have applications in the field of query optimization by suggesting query rewrites. Also, the existence of an OD in a table can provide hints on which integrity constraints are valid for the domain of the data at hand. This work is the first to describe the discovery problem for order dependencies in a principled manner by characterizing the search space, developing and proving pruning rules, and presenting the algorithm Order, which finds all order dependencies in a given table. Order traverses the lattice of permutations of attributes in a level-wise bottom-up manner. In a comprehensive evaluation we show that it is efficient even for various large datasets. <p> Szlichta et al. propose a more efficient algorithm to discover order dependencies. In their paper they also point out flaws of our proposal:<br> Jaroslaw Szlichta, Parke Godfrey, Lukasz Golab, Mehdi Kargar, Divesh Srivastava: <a href="http://www.vldb.org/pvldb/vol10/p721-szlichta.pdf">Effective and Complete Discovery of Order Dependencies via Set-based Axiomatization</a>, in PVLDB 10(7), p. 721 - 732, 2017.}, author = {Langer, Philipp and Naumann, Felix}, journal = {VLDB Journal}, keywords = {isg}, number = 2, pages = {223-241}, title = {Efficient Order Dependency Discovery}, volume = 25, year = 2016 }

2015

• Kruse, S., Papenbrock, T., Naumann, F.: Scaling Out the Discovery of Inclusion Dependencies. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW). pp. 445-454 (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Inclusion dependencies are among the most important database dependencies. In addition to their most prominent application – foreign key discovery – inclusion dependencies are an important input to data integration, query optimization, and schema redesign. With their discovery being a recurring data profiling task, previous research has proposed different algorithms to discover all inclusion dependencies within a given dataset. However, none of the proposed algorithms is designed to scale out, i.e., none can be distributed across multiple nodes in a computer cluster to increase the performance. So on large datasets with many inclusion dependencies, these algorithms can take days to complete, even on high-performance computers. We introduce SINDY, an algorithm that efficiently discovers all unary inclusion dependencies of a given relational dataset in a distributed fashion and that is not tied to main memory requirements. We give a practical implementation of SINDY that builds upon the map-reduce-style framework Stratosphere and conduct several experiments showing that SINDY can process huge datasets by several factors faster than its competitors while scaling with the number of cluster nodes.
  @inproceedings{kruse2015scaling, abstract = {Inclusion dependencies are among the most important database dependencies. In addition to their most prominent application – foreign key discovery – inclusion dependencies are an important input to data integration, query optimization, and schema redesign. With their discovery being a recurring data profiling task, previous research has proposed different algorithms to discover all inclusion dependencies within a given dataset. However, none of the proposed algorithms is designed to scale out, i.e., none can be distributed across multiple nodes in a computer cluster to increase the performance. So on large datasets with many inclusion dependencies, these algorithms can take days to complete, even on high-performance computers. We introduce SINDY, an algorithm that efficiently discovers all unary inclusion dependencies of a given relational dataset in a distributed fashion and that is not tied to main memory requirements. We give a practical implementation of SINDY that builds upon the map-reduce-style framework Stratosphere and conduct several experiments showing that SINDY can process huge datasets by several factors faster than its competitors while scaling with the number of cluster nodes.}, author = {Kruse, Sebastian and Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW)}, keywords = {inclusion_dependencies parallel disctributed profiling discovery isg}, pages = {445-454}, title = {Scaling Out the Discovery of Inclusion Dependencies}, year = 2015 }
• Krestel, R., Werkmeister, T., Wiradarma, T.P., Kasneci, G.: Tweet-Recommender: Finding Relevant Tweets for News Articles. Proceedings of the 24th International World Wide Web Conference (WWW). ACM (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Twitter has become a prime source for disseminating news and opinions. However, the length of tweets prohibits detailed descriptions, instead, tweets sometimes contain URLs that link to detailed news articles. In this paper, we devise generic techniques for recommending tweets for any given news article. To evaluate and compare the different techniques, we collected tens of thousands of tweets and news articles and conducted a user study on the relevance of recommendations.
  @inproceedings{krestel15www, abstract = {Twitter has become a prime source for disseminating news and opinions. However, the length of tweets prohibits detailed descriptions, instead, tweets sometimes contain URLs that link to detailed news articles. In this paper, we devise generic techniques for recommending tweets for any given news article. To evaluate and compare the different techniques, we collected tens of thousands of tweets and news articles and conducted a user study on the relevance of recommendations.}, author = {Krestel, Ralf and Werkmeister, Thomas and Wiradarma, Timur Pratama and Kasneci, Gjergji}, booktitle = {Proceedings of the 24th International World Wide Web Conference (WWW)}, keywords = {news_analysis web_science social_media isg}, month = 5, publisher = {ACM}, title = {Tweet-Recommender: Finding Relevant Tweets for News Articles}, year = 2015 }
• Kruse, S., Papotti, P., Naumann, F.: Estimating Data Integration and Cleaning Effort. Proceedings of the International Conference on Extending Database Technology (EDBT) (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Data cleaning and data integration have been the topic of intensive research for at least the past thirty years, resulting in a multitude of specialized methods and integrated tool suites. All of them require at least some and in most cases significant human input in their configuration, during processing, and for evaluation. For managers (and for developers and scientists) it would be therefore of great value to be able to estimate the effort of cleaning and integrating some given data sets and to know the pitfalls of such an integration project in advance. This helps deciding about an integration project using cost/benefit analysis, budgeting a team with funds and manpower, and monitoring its progress. Further, knowledge of how well a data source fits into a given data ecosystem improves source selection. We present an extensible framework for the automatic effort estimation for mapping and cleaning activities in data integration projects with multiple sources. It comprises a set of measures and methods for estimating integration complexity and ultimately effort, taking into account heterogeneities of both schemas and instances and regarding both integration and cleaning operations. Experiments on two real-world scenarios show that our proposal is two to four times more accurate than a current approach in estimating the time duration of an integration process, and provides a meaningful breakdown of the integration problems as well as the required integration activitiesr nodes.
  @inproceedings{kruse2015estimating, abstract = {Data cleaning and data integration have been the topic of intensive research for at least the past thirty years, resulting in a multitude of specialized methods and integrated tool suites. All of them require at least some and in most cases significant human input in their configuration, during processing, and for evaluation. For managers (and for developers and scientists) it would be therefore of great value to be able to estimate the effort of cleaning and integrating some given data sets and to know the pitfalls of such an integration project in advance. This helps deciding about an integration project using cost/benefit analysis, budgeting a team with funds and manpower, and monitoring its progress. Further, knowledge of how well a data source fits into a given data ecosystem improves source selection. We present an extensible framework for the automatic effort estimation for mapping and cleaning activities in data integration projects with multiple sources. It comprises a set of measures and methods for estimating integration complexity and ultimately effort, taking into account heterogeneities of both schemas and instances and regarding both integration and cleaning operations. Experiments on two real-world scenarios show that our proposal is two to four times more accurate than a current approach in estimating the time duration of an integration process, and provides a meaningful breakdown of the integration problems as well as the required integration activitiesr nodes.}, author = {Kruse, Sebastian and Papotti, Paolo and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Extending Database Technology (EDBT)}, keywords = {isg}, title = {Estimating Data Integration and Cleaning Effort}, year = 2015 }
• Jentzsch, A., Dullweber, C., Troiano, P., Naumann, F.: Exploring Linked Data Graph Structures. In Proceedings of Posters and Demos Session, ISWC2015. , Bethlehem, PA, USA (2015).
  [ BibTeX ]
   
  @inproceedings{jentzsch_iswc_pd_2015, address = {Bethlehem, PA, USA}, author = {Jentzsch, Anja and Dullweber, Christian and Troiano, Pierpaolo and Naumann, Felix}, booktitle = {In Proceedings of Posters and Demos Session, ISWC2015}, keywords = {linkeddata profiling isg}, title = {Exploring Linked Data Graph Structures}, year = 2015 }
• Jentzsch, A., Mühleisen, H., Naumann, F.: Uniqueness, Density, and Keyness: Exploring Class Hierarchies. In Proceedings of 6th International Workshop on Consuming Linked Data (COLD 2015), ISWC 2015. , Bethlehem, PA, USA (2015).
  [ BibTeX ]
   
  @inproceedings{jentzsch_iswc_cold_2015, address = {Bethlehem, PA, USA}, author = {Jentzsch, Anja and Mühleisen, Hannes and Naumann, Felix}, booktitle = {In Proceedings of 6th International Workshop on Consuming Linked Data (COLD 2015), ISWC 2015}, keywords = {isg}, title = {Uniqueness, Density, and Keyness: Exploring Class Hierarchies}, year = 2015 }
• Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., Naumann, F.: A Serendipity Model For News Recommendation. KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings. pp. 111-123. Springer (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Recommendation algorithms typically work by suggesting items that are similar to the ones that a user likes, or items that similar users like. We propose a content-based recommendation technique with the focus on serendipity of news recommendations. Serendipitous recommendations have the characteristic of being unexpected yet fortunate and interesting to the user, and thus might yield higher user satisfaction. In our work, we explore the concept of serendipity in the area of news articles and propose a general framework that incorporates the benefits of serendipity- and similarity-based recommendation techniques. An evaluation against other baseline recommendation models is carried out in a user study.
  @inproceedings{jenders2015ki, abstract = {Recommendation algorithms typically work by suggesting items that are similar to the ones that a user likes, or items that similar users like. We propose a content-based recommendation technique with the focus on serendipity of news recommendations. Serendipitous recommendations have the characteristic of being unexpected yet fortunate and interesting to the user, and thus might yield higher user satisfaction. In our work, we explore the concept of serendipity in the area of news articles and propose a general framework that incorporates the benefits of serendipity- and similarity-based recommendation techniques. An evaluation against other baseline recommendation models is carried out in a user study.}, author = {Jenders, Maximilian and Lindhauer, Thorben and Kasneci, Gjergji and Krestel, Ralf and Naumann, Felix}, booktitle = {KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings}, keywords = {news_analysis web_science isg}, month = 9, pages = {111-123}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {A Serendipity Model For News Recommendation}, volume = 9324, year = 2015 }
• Schmidt, D., Frohnhofen, J., Knebel, S., Meinel, F., Perchyk, M., Risch, J., Striebel, J., Wachtel, J., Baudisch, P.: Ergonomic Interaction for Touch Floors. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. p. 3879--3888. ACM, Seoul, Republic of Korea (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  The main appeal of touch floors is that they are the only direct touch form factor that scales to arbitrary size, therefore allowing direct touch to scale to very large numbers of display objects. In this paper, however, we argue that the price for this benefit is bad physical ergonomics: prolonged standing, especially in combination with looking down, quickly causes fatigue and repetitive strain. We propose addressing this issue by allowing users to operate touch floors in any pose they like, including sitting and lying. To allow users to transition between poses seamlessly, we present a simple pose-aware view manager that supports users by adjusting the entire view to the new pose. We support the main assumption behind the work with a simple study that shows that several poses are indeed more ergonomic for touch floor interaction than standing. We ground the design of our view manager by analyzing, which screen regions users can see and touch in each of the respective poses.
  @inproceedings{Schmidt:2015:EIT:2702123.2702254, abstract = {The main appeal of touch floors is that they are the only direct touch form factor that scales to arbitrary size, therefore allowing direct touch to scale to very large numbers of display objects. In this paper, however, we argue that the price for this benefit is bad physical ergonomics: prolonged standing, especially in combination with looking down, quickly causes fatigue and repetitive strain. We propose addressing this issue by allowing users to operate touch floors in any pose they like, including sitting and lying. To allow users to transition between poses seamlessly, we present a simple pose-aware view manager that supports users by adjusting the entire view to the new pose. We support the main assumption behind the work with a simple study that shows that several poses are indeed more ergonomic for touch floor interaction than standing. We ground the design of our view manager by analyzing, which screen regions users can see and touch in each of the respective poses.}, address = {New York, NY, USA}, author = {Schmidt, Dominik and Frohnhofen, Johannes and Knebel, Sven and Meinel, Florian and Perchyk, Mariya and Risch, Julian and Striebel, Jonathan and Wachtel, Julia and Baudisch, Patrick}, booktitle = {Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems}, keywords = {hpi}, pages = {3879--3888}, publisher = {ACM}, series = {CHI '15}, title = {Ergonomic Interaction for Touch Floors}, year = 2015 }
• Roick, M., Jenders, M., Krestel, R.: How to Stay Up-to-date on Twitter with General Keywords. Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. CEUR-WS.org (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Microblogging platforms make it easy for users to share information through the publication of short personal messages. However, users are not only interested in sharing, but even more so in consuming information. As a result, they are confronted with new challenges when it comes to retrieving information on microblogging platforms. In this paper we present a query expansion method based on latent topics to support users interested in topical information. Similar to news aggregator sites, our approach identifies subtopics to a given query and provides the user with a quick overview of discussed topics within the microblogging platform. Using a document collection of microblog posts from Twitter as an exemplary microblogging platform, we compare the quality of search results returned by our algorithm with a baseline approach and a state-of-the-art microblog-specific query expansion method. To this end, we introduce a novel, innovative semi-supervised evaluation strategy based on expert Twitter users. In contrast to existing query expansion methods, our approach can be used to aggregate and visualize topical query results based on the calculated topic models, while achieving competitive results for traditional keyword-based search with regards to mean average precision.
  @inproceedings{roick2015lwa, abstract = {Microblogging platforms make it easy for users to share information through the publication of short personal messages. However, users are not only interested in sharing, but even more so in consuming information. As a result, they are confronted with new challenges when it comes to retrieving information on microblogging platforms. In this paper we present a query expansion method based on latent topics to support users interested in topical information. Similar to news aggregator sites, our approach identifies subtopics to a given query and provides the user with a quick overview of discussed topics within the microblogging platform. Using a document collection of microblog posts from Twitter as an exemplary microblogging platform, we compare the quality of search results returned by our algorithm with a baseline approach and a state-of-the-art microblog-specific query expansion method. To this end, we introduce a novel, innovative semi-supervised evaluation strategy based on expert Twitter users. In contrast to existing query expansion methods, our approach can be used to aggregate and visualize topical query results based on the calculated topic models, while achieving competitive results for traditional keyword-based search with regards to mean average precision.}, author = {Roick, Mandy and Jenders, Maximilian and Krestel, Ralf}, booktitle = {Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB}, keywords = {web_science social_media isg}, month = 10, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {How to Stay Up-to-date on Twitter with General Keywords}, volume = 1458, year = 2015 }
• Hennig, P., Berger, P., Dullweber, C., Finke, M., Maschler, F., Risch, J., Meinel, C.: Social Media Story Telling. Proceedings of the 8th IEEE International Conference on Social Computing and Networking (SocialCom2015). , Chengdu, China (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  The number of documents on the web increases rapidly and often there is an enormous information overlap between different sources covering the same topic. Since it is impractical to read through all posts regarding a subject, there is a need for summaries combining the most relevant facts. In this context combining information from different sources in form of stories is an important method to provide perspective, while presenting and enriching the existing content in an interesting, natural and narrative way. Today, stories are often not available or they have been elaborately written and selected by journalists. Thus, we present an automated approach to create stories from multiple input documents. Furthermore the developed framework implements strategies to visualize stories and link content to related sources of information, such as images, tweets and encyclopedia records ready to be explored by the reader. Our approach combines deriving a story line from a graph of interlinked sources with a story-centric multi-document summarization.
  @inproceedings{2015_Hennig_SocialCom, abstract = {The number of documents on the web increases rapidly and often there is an enormous information overlap between different sources covering the same topic. Since it is impractical to read through all posts regarding a subject, there is a need for summaries combining the most relevant facts. In this context combining information from different sources in form of stories is an important method to provide perspective, while presenting and enriching the existing content in an interesting, natural and narrative way. Today, stories are often not available or they have been elaborately written and selected by journalists. Thus, we present an automated approach to create stories from multiple input documents. Furthermore the developed framework implements strategies to visualize stories and link content to related sources of information, such as images, tweets and encyclopedia records ready to be explored by the reader. Our approach combines deriving a story line from a graph of interlinked sources with a story-centric multi-document summarization.}, address = {Chengdu, China}, author = {Hennig, Patrick and Berger, Philipp and Dullweber, Christian and Finke, Moritz and Maschler, Fabian and Risch, Julian and Meinel, Christoph}, booktitle = {Proceedings of the 8th IEEE International Conference on Social Computing and Networking (SocialCom2015)}, keywords = {hpi myown}, month = 12, title = {Social Media Story Telling}, year = 2015 }
• Schubotz, T., Krestel, R.: Online Temporal Summarization of News Events. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). pp. 679-684. IEEE Computer Society (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Nowadays, an ever increasing number of news articles is published on a daily basis. Especially after notable national and international events or disasters, news coverage rises tremendously. Temporal summarization is an approach to automatically summarize such information in a timely manner. Summaries are created incrementally with progressing time, as soon as new information is available. Given a user-defined query, we designed a temporal summarizer based on probabilistic language models and entity recognition. First, all relevant documents and sentences are extracted from a stream of news documents using BM25 scoring. Second, a general query language model is created which is used to detect typical sentences respective to the query with Kullback-Leibler divergence. Based on the retrieval result, this query model is extended over time by terms appearing frequently during the particular event. Our system is evaluated with a document corpus including test data provided by the Text Retrieval Conference (TREC).
  @inproceedings{wi15, abstract = {Nowadays, an ever increasing number of news articles is published on a daily basis. Especially after notable national and international events or disasters, news coverage rises tremendously. Temporal summarization is an approach to automatically summarize such information in a timely manner. Summaries are created incrementally with progressing time, as soon as new information is available. Given a user-defined query, we designed a temporal summarizer based on probabilistic language models and entity recognition. First, all relevant documents and sentences are extracted from a stream of news documents using BM25 scoring. Second, a general query language model is created which is used to detect typical sentences respective to the query with Kullback-Leibler divergence. Based on the retrieval result, this query model is extended over time by terms appearing frequently during the particular event. Our system is evaluated with a document corpus including test data provided by the Text Retrieval Conference (TREC).}, author = {Schubotz, Tobias and Krestel, Ralf}, booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)}, keywords = {news_analysis web_science isg}, month = 12, pages = {679-684}, publisher = {IEEE Computer Society}, title = {Online Temporal Summarization of News Events}, year = 2015 }
• Gruetze, T., Yao, G., Krestel, R.: Learning Temporal Tagging Behaviour. Proceedings of the 24th International Conference on World Wide Web Companion (WWW). p. 1333--1338. ACM (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags.
  @inproceedings{Gruetze2015TempWeb, abstract = {Social networking services, such as Facebook, Google+, and Twitter are commonly used to share relevant Web documents with a peer group. By sharing a document with her peers, a user recommends the content for others and annotates it with a short description text. This short description yield many chances for text summarization and categorization. Because today’s social networking platforms are real-time media, the sharing behaviour is subject to many temporal effects, i.e., current events, breaking news, and trending topics. In this paper, we focus on time-dependent hashtag usage of the Twitter community to annotate shared Web-text documents. We introduce a framework for time-dependent hashtag recommendation models and introduce two content-based models. Finally, we evaluate the introduced models with respect to recommendation quality based on a Twitter-dataset consisting of links to Web documents that were aligned with hashtags.}, author = {Gruetze, Toni and Yao, Gary and Krestel, Ralf}, booktitle = {Proceedings of the 24th International Conference on World Wide Web Companion (WWW)}, keywords = {web_science social_media isg}, month = 5, pages = {1333--1338}, publisher = {ACM}, title = {Learning Temporal Tagging Behaviour}, year = 2015 }
• Papenbrock, T., Kruse, S., Quiane-Ruiz, J.-A., Naumann, F.: Divide & Conquer-based Inclusion Dependency Discovery. Proceedings of the VLDB Endowmen. 8, 774-785 (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  The discovery of all inclusion dependencies (INDs) in a dataset is an important part of any data profiling effort. Apart from the detection of foreign key relationships, INDs can help to perform data integration, query optimization, integrity checking, or schema (re-)design. However, the detection of INDs gets harder as datasets become larger in terms of number of tuples as well as attributes. To this end, we propose BINDER, an IND detection system that is capable of detecting both unary and n-ary INDs. It is based on a divide & conquer approach, which allows to handle very large datasets – an important property on the face of the ever increasing size of today’s data. In contrast to most related works, we do not rely on existing database functionality nor assume that inspected datasets fit into main memory. This renders BINDER an efficient and scalable competitor. Our exhaustive experimental evaluation shows the high superiority of BINDER over the state-of-the-art in both unary (SPIDER) and n-ary (MIND) IND discovery. BINDER is up to 26x faster than SPIDER and more than 2500x faster than MIND.
  @article{papenbrock2015binder, abstract = {The discovery of all inclusion dependencies (INDs) in a dataset is an important part of any data profiling effort. Apart from the detection of foreign key relationships, INDs can help to perform data integration, query optimization, integrity checking, or schema (re-)design. However, the detection of INDs gets harder as datasets become larger in terms of number of tuples as well as attributes. To this end, we propose BINDER, an IND detection system that is capable of detecting both unary and n-ary INDs. It is based on a divide & conquer approach, which allows to handle very large datasets – an important property on the face of the ever increasing size of today’s data. In contrast to most related works, we do not rely on existing database functionality nor assume that inspected datasets fit into main memory. This renders BINDER an efficient and scalable competitor. Our exhaustive experimental evaluation shows the high superiority of BINDER over the state-of-the-art in both unary (SPIDER) and n-ary (MIND) IND discovery. BINDER is up to 26x faster than SPIDER and more than 2500x faster than MIND.}, author = {Papenbrock, Thorsten and Kruse, Sebastian and Quiane-Ruiz, Jorge-Arnulfo and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Very Large Data Bases (PVLDB)}, journal = {Proceedings of the VLDB Endowmen}, keywords = {hpi inclusion_dependencies profiling discovery isg binder divide_and_conquer}, month = {aug}, number = 7, pages = {774-785}, publisher = {VLDB Endowment}, title = {Divide & Conquer-based Inclusion Dependency Discovery}, volume = 8, year = 2015 }
• Papenbrock, T., Heise, A., Naumann, F.: Progressive Duplicate Detection. IEEE Transactions on Knowledge and Data Engineering (TKDE). 27, 1316-1329 (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Duplicate detection is the process of identifying multiple representations of same real world entities. Today, duplicate detection methods need to process ever larger datasets in ever shorter time: maintaining the quality of a dataset becomes increasingly difficult. We present two novel, progressive duplicate detection algorithms that significantly increase the efficiency of finding duplicates if the execution time is limited: They maximize the gain of the overall process within the time available by reporting most results much earlier than traditional approaches. Comprehensive experiments show that our progressive algorithms can double the efficiency over time of traditional duplicate detection and significantly improve upon related work.
  @article{progressive_dude2015, abstract = {Duplicate detection is the process of identifying multiple representations of same real world entities. Today, duplicate detection methods need to process ever larger datasets in ever shorter time: maintaining the quality of a dataset becomes increasingly difficult. We present two novel, progressive duplicate detection algorithms that significantly increase the efficiency of finding duplicates if the execution time is limited: They maximize the gain of the overall process within the time available by reporting most results much earlier than traditional approaches. Comprehensive experiments show that our progressive algorithms can double the efficiency over time of traditional duplicate detection and significantly improve upon related work.}, author = {Papenbrock, Thorsten and Heise, Arvid and Naumann, Felix}, journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)}, keywords = {duplicate_detection hpi data_cleansing isg}, number = 5, pages = {1316-1329}, publisher = {IEEE Computer Society}, title = {Progressive Duplicate Detection}, volume = 27, year = 2015 }
• Abedjan, Z., Golab, L., Naumann, F.: Profiling relational data: a survey. VLDB Journal. 24, 557-581 (2015).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.
  @article{AbedjanSurvey15, abstract = {Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases.}, author = {Abedjan, Ziawasch and Golab, Lukasz and Naumann, Felix}, journal = {VLDB Journal}, keywords = {hpi profiling isg}, number = 4, pages = {557-581}, title = {Profiling relational data: a survey}, volume = 24, year = 2015 }
• Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data Profiling with Metanome. Proceedings of the VLDB Endowment. 8, 1860-1871 (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies. We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic discovery of complex metadata. Metanome’s goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the at times large metadata sets.
  @article{papenbrock2015metanome, abstract = {Data profiling is the discipline of discovering metadata about given datasets. The metadata itself serve a variety of use cases, such as data integration, data cleansing, or query optimization. Due to the importance of data profiling in practice, many tools have emerged that support data scientists and IT professionals in this task. These tools provide good support for profiling statistics that are easy to compute, but they are usually lacking automatic and efficient discovery of complex statistics, such as inclusion dependencies, unique column combinations, or functional dependencies. We present Metanome, an extensible profiling platform that incorporates many state-of-the-art profiling algorithms. While Metanome is able to calculate simple profiling statistics in relational data, its focus lies on the automatic discovery of complex metadata. Metanome’s goal is to provide novel profiling algorithms from research, perform comparative evaluations, and to support developers in building and testing new algorithms. In addition, Metanome is able to rank profiling results according to various metrics and to visualize the at times large metadata sets.}, author = {Papenbrock, Thorsten and Bergmann, Tanja and Finke, Moritz and Zwiener, Jakob and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Very Large Data Bases (PVLDB)}, journal = {Proceedings of the VLDB Endowment}, keywords = {hpi order_dependencies basic_statistics inclusion_dependencies unique_column_combinations multivalued_dependencies profiling isg functional_dependencies metanome}, month = {aug}, number = 12, pages = {1860-1871}, publisher = {VLDB Endowment}, title = {Data Profiling with Metanome}, volume = 8, year = 2015 }
• Krestel, R., Dokoohaki, N.: Diversifying Customer Review Rankings. Neural Networks. 66, 36-45 (2015).
  [ Abstract ] [ BibTeX ]
   
  E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review. In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review’s sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations.
  @article{krestel15nn, abstract = {E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review. In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review’s sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations.}, author = {Krestel, Ralf and Dokoohaki, Nima}, journal = {Neural Networks}, keywords = {web_science isg}, month = 2, organization = {Elsevier}, pages = {36-45}, title = {Diversifying Customer Review Rankings}, volume = 66, year = 2015 }
• Papenbrock, T., Ehrlich, J., Marten, J., Neubert, T., Rudolph, J.-P., Schönberg, M., Zwiener, J., Naumann, F.: Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms. Proceedings of the VLDB Endowment. 8, 1082-1093 (2015).
  [ Abstract ] [ BibTeX ] [ URL ] [ Download ]
   
  Functional dependencies are important metadata used for schema normalization, data cleansing and many other tasks. The efficient discovery of functional dependencies in tables is a well-known challenge in database research and has seen several approaches. Because no comprehensive comparison between these algorithms exist at the time, it is hard to choose the best algorithm for a given dataset. In this experimental paper, we describe, evaluate, and compare the seven most cited and most important algorithms, all solving this same problem. First, we classify the algorithms into three different categories, explaining their commonalities. We then describe all algorithms with their main ideas. The descriptions provide additional details where the original papers were ambiguous or incomplete. Our evaluation of careful re-implementations of all algorithms spans a broad test space including synthetic and real-world data. We show that all functional dependency algorithms optimize for certain data characteristics and provide hints on when to choose which algorithm. In summary, however, all current approaches scale surprisingly poorly, showing potential for future research.
  @article{papenbrock2015fdeval, abstract = {Functional dependencies are important metadata used for schema normalization, data cleansing and many other tasks. The efficient discovery of functional dependencies in tables is a well-known challenge in database research and has seen several approaches. Because no comprehensive comparison between these algorithms exist at the time, it is hard to choose the best algorithm for a given dataset. In this experimental paper, we describe, evaluate, and compare the seven most cited and most important algorithms, all solving this same problem. First, we classify the algorithms into three different categories, explaining their commonalities. We then describe all algorithms with their main ideas. The descriptions provide additional details where the original papers were ambiguous or incomplete. Our evaluation of careful re-implementations of all algorithms spans a broad test space including synthetic and real-world data. We show that all functional dependency algorithms optimize for certain data characteristics and provide hints on when to choose which algorithm. In summary, however, all current approaches scale surprisingly poorly, showing potential for future research.}, author = {Papenbrock, Thorsten and Ehrlich, Jens and Marten, Jannik and Neubert, Tommy and Rudolph, Jan-Peer and Schönberg, Martin and Zwiener, Jakob and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Very Large Data Bases (PVLDB)}, journal = {Proceedings of the VLDB Endowment}, keywords = {hpi functional_dependency_discovery evaluation profiling isg functional_dependencies}, month = {jun}, number = 10, pages = {1082-1093}, publisher = {VLDB Endowment}, title = {Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms}, volume = 8, year = 2015 }
• Rheinländer, A., Heise, A., Hueske, F., Leser, U., Naumann, F.: SOFA: An Extensible Logical Optimizer for UDF-heavy Data Flows. Information Systems. 52, 96-125 (2015).
  [ Abstract ] [ BibTeX ]
   
  Recent years have seen an increased interest in large-scale analytical data flows on non-relational data. These data flows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant building blocks of such data flows are user-defined predicates or functions (UDFs). However, the heavy use of UDFs is not well taken into account for data flow optimization in current systems. SOFA is a novel and extensible optimizer for UDF-heavy data flows. It builds on a concise set of properties for describing the semantics of Map/Reduce-style UDFs and a small set of rewrite rules, which use these properties to find a much larger number of semantically equivalent plan rewrites than possible with traditional techniques. A salient feature of our approach is extensibility: We arrange user-defined operators and their properties into a subsumption hierarchy, which considerably eases integration and optimization of new operators. We evaluate SOFA on a selection of UDF-heavy data flows from different domains and compare its performance to three other algorithms for data flow optimization. Our experiments reveal that SOFA finds efficient plans, outperforming the best plans found by its competitors by a factor of up to six.
  @article{Rheinlaender15, abstract = {Recent years have seen an increased interest in large-scale analytical data flows on non-relational data. These data flows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant building blocks of such data flows are user-defined predicates or functions (UDFs). However, the heavy use of UDFs is not well taken into account for data flow optimization in current systems. SOFA is a novel and extensible optimizer for UDF-heavy data flows. It builds on a concise set of properties for describing the semantics of Map/Reduce-style UDFs and a small set of rewrite rules, which use these properties to find a much larger number of semantically equivalent plan rewrites than possible with traditional techniques. A salient feature of our approach is extensibility: We arrange user-defined operators and their properties into a subsumption hierarchy, which considerably eases integration and optimization of new operators. We evaluate SOFA on a selection of UDF-heavy data flows from different domains and compare its performance to three other algorithms for data flow optimization. Our experiments reveal that SOFA finds efficient plans, outperforming the best plans found by its competitors by a factor of up to six.}, author = {Rheinländer, Astrid and Heise, Arvid and Hueske, Fabian and Leser, Ulf and Naumann, Felix}, journal = {Information Systems}, keywords = {isg}, pages = {96-125}, title = {SOFA: An Extensible Logical Optimizer for UDF-heavy Data Flows}, volume = 52, year = 2015 }

2014

• Meyer, A., Pufahl, L., Batoulis, K., Kruse, S., Lindhauer, T., Stoff, T., Fahland, D., Weske, M.: Data Perspective in Process Choreographies: Modeling and Execution. 26th International Conference on Advanced Information Systems Engineering. , Thessaloniki, Greece (2014).
  [ BibTeX ]
   
  @inproceedings{meyer2014data, address = {Thessaloniki, Greece}, author = {Meyer, Andreas and Pufahl, Luise and Batoulis, Kimon and Kruse, Sebastian and Lindhauer, Thorben and Stoff, Thomas and Fahland, Dirk and Weske, Mathias}, booktitle = {26th International Conference on Advanced Information Systems Engineering}, keywords = {isg}, title = {Data Perspective in Process Choreographies: Modeling and Execution}, year = 2014 }
• Abedjan, Z., Quanie-Ruiz, J.-A., Naumann, F.: Detecting Unique Column Combinations on Dynamic Data. Proceedings of the IEEE International Conference on Data Engineering (ICDE). , Chicago, IL (2014).
  [ BibTeX ] [ Download ]
   
  @inproceedings{abedjan_icde_2014, address = {Chicago, IL}, author = {Abedjan, Ziawasch and Quanie-Ruiz, Jorge-Arnulfo and Naumann, Felix}, booktitle = {Proceedings of the IEEE International Conference on Data Engineering (ICDE)}, keywords = {isg}, title = {Detecting Unique Column Combinations on Dynamic Data}, year = 2014 }
• Forchhammer, B., Jentzsch, A., Naumann, F.: LODOP - Multi-Query Optimization for Linked Data Profiling Queries. In Proceedings of the International Workshop on Dataset PROFIling & fEderated Search for Linked Data (PROFILES) in conjunction with ESWC. , Heraklion, Greece (2014).
  [ BibTeX ]
   
  @inproceedings{forchhammer_profiles_2014, address = {Heraklion, Greece}, author = {Forchhammer, Benedikt and Jentzsch, Anja and Naumann, Felix}, booktitle = {In Proceedings of the International Workshop on Dataset PROFIling & fEderated Search for Linked Data (PROFILES) in conjunction with ESWC.}, keywords = {isg}, note = {Selected for Best Workshop Paper Award.}, title = {LODOP - Multi-Query Optimization for Linked Data Profiling Queries.}, year = 2014 }
• Rheinländer, A., Beckmann, M., Kunkel, A., Heise, A., Stoltmann, T., Leser, U.: Versatile optimization of UDF-heavy data flows with SOFA (demo). Proceedings of the SIGMOD conference. pp. 685-688 (2014).
  [ BibTeX ]
   
  @inproceedings{RheinlanderBKHSL14, author = {Rheinländer, Astrid and Beckmann, Martin and Kunkel, Anja and Heise, Arvid and Stoltmann, Thomas and Leser, Ulf}, booktitle = {Proceedings of the SIGMOD conference}, keywords = {isg}, pages = {685-688}, title = {Versatile optimization of UDF-heavy data flows with SOFA (demo)}, year = 2014 }
• Abedjan, Z., Gruetze, T., Jentzsch, A., Naumann, F.: Profiling and Mining RDF Data with ProLOD++. Proceedings of the IEEE International Conference on Data Engineering (ICDE), Demo. , Chicago, IL (2014).
  [ BibTeX ] [ Download ]
   
  @inproceedings{prolodplusplus_icde_2014, address = {Chicago, IL}, author = {Abedjan, Ziawasch and Gruetze, Toni and Jentzsch, Anja and Naumann, Felix}, booktitle = {Proceedings of the IEEE International Conference on Data Engineering (ICDE), Demo}, keywords = {hpi lod rdfmining profiling isg}, title = {Profiling and Mining RDF Data with ProLOD++}, year = 2014 }
• Gruetze, T., Kasneci, G., Zuo, Z., Naumann, F.: Bootstrapping Wikipedia to Answer Ambiguous Person Name Queries. 10th International Workshop on Information Integration on the Web (IIWeb). , Chicago, IL (2014).
  [ BibTeX ] [ Download ]
   
  @inproceedings{GruetzeIIWeb14, address = {Chicago, IL}, author = {Gruetze, Toni and Kasneci, Gjergji and Zuo, Zhe and Naumann, Felix}, booktitle = {10th International Workshop on Information Integration on the Web (IIWeb)}, keywords = {isg}, title = {Bootstrapping Wikipedia to Answer Ambiguous Person Name Queries}, year = 2014 }
• Heise, A., Kasneci, G., Naumann, F.: Estimating the Number and Sizes of Fuzzy-Duplicate Clusters. Proceedings of the Conference on Information and Knowledge Management (CIKM). pp. 959-968 (2014).
  [ BibTeX ] [ Download ]
   
  @inproceedings{DuplicityEstimation, author = {Heise, Arvid and Kasneci, Gjergji and Naumann, Felix}, booktitle = {Proceedings of the Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {959-968}, title = {Estimating the Number and Sizes of Fuzzy-Duplicate Clusters}, year = 2014 }
• Abedjan, Z., Naumann, F.: Amending RDF Entities with New Facts. Know@LOD Workshop in conjunction with ESWC. , Creete, Greece (2014).
  [ BibTeX ]
   
  @inproceedings{abedjan_knowatlod_2014, address = {Creete, Greece}, author = {Abedjan, Ziawasch and Naumann, Felix}, booktitle = {Know@LOD Workshop in conjunction with ESWC}, keywords = {hpi lod rdfmining isg}, note = {Selected for Best Workshop Paper Award.}, title = {Amending RDF Entities with New Facts}, year = 2014 }
• Abedjan, Z., Schulze, P., Naumann, F.: DFD: Efficient Discovery of Functional Dependencies. In Proceedings of the International Conference on Information and Knowledge Management (CIKM), Shanghai, China. pp. 949-958 (2014).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets. Winner of the CIKM 2014 Best Student Paper Award
  @inproceedings{dfd2014, abstract = {The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets. Winner of the CIKM 2014 Best Student Paper Award}, author = {Abedjan, Ziawasch and Schulze, Patrick and Naumann, Felix}, booktitle = {In Proceedings of the International Conference on Information and Knowledge Management (CIKM), Shanghai, China}, keywords = {hpi profiling isg}, pages = {949-958}, title = {DFD: Efficient Discovery of Functional Dependencies}, year = 2014 }
• Langer, P., Schulze, P., George, S., Kohnen, M., Metzke, T., Abedjan, Z., Kasneci, G.: Assigning Global Relevance Scores to DBpedia Facts. International Workshop on Data Engineering meets the Semantic Web (DESWeb). , Chicago, IL (2014).
  [ BibTeX ] [ Download ]
   
  @inproceedings{rankfacts_dewsweb_2014, address = {Chicago, IL}, author = {Langer, Philipp and Schulze, Patrick and George, Stefan and Kohnen, Matthias and Metzke, Tobias and Abedjan, Ziawasch and Kasneci, Gjergji}, booktitle = {International Workshop on Data Engineering meets the Semantic Web (DESWeb)}, keywords = {LOD hpi isg}, title = {Assigning Global Relevance Scores to DBpedia Facts}, year = 2014 }
• Zuo, Z., Kasneci, G., Gruetze, T., Naumann, F.: BEL: Bagging for Entity Linking. 25th International Conference on Computational Linguistics (COLING). , Dublin, Ireland (2014).
  [ BibTeX ] [ Download ]
   
  @inproceedings{ZuoColing14, address = {Dublin, Ireland}, author = {Zuo, Zhe and Kasneci, Gjergji and Gruetze, Toni and Naumann, Felix}, booktitle = {25th International Conference on Computational Linguistics (COLING)}, keywords = {isg}, title = {BEL: Bagging for Entity Linking}, year = 2014 }
• Vogel, T., Naumann, F.: Semi-Supervised Consensus Clustering: Reducing Human Effort. Proceedings of the International Workshop on Data Integration and Applications (2014).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Machine-based clustering yields fuzzy results. For example, when detecting duplicates in a dataset, different tools might end up with different clusterings. Eventually, a decision needs to be made, defining which records are in the same cluster, i. e., are duplicates. Such a definitive result is called a Consensus Clustering and can be created by evaluating the clustering attempts against each other and only resolving the disagreements by human experts. Yet, there can be different consensus clusterings, depending on the choice of disagreements presented to the human expert. In particular, they may require a different number of manual inspections. We present a set of strategies to select the smallest set of manual inspections to arrive at a consensus clustering and evaluate their efficiency on a set of real-world and synthetic datasets.
  @inproceedings{ConsensusClusteringVogelNaumannDINA2014, abstract = {Machine-based clustering yields fuzzy results. For example, when detecting duplicates in a dataset, different tools might end up with different clusterings. Eventually, a decision needs to be made, defining which records are in the same cluster, i. e., are duplicates. Such a definitive result is called a Consensus Clustering and can be created by evaluating the clustering attempts against each other and only resolving the disagreements by human experts. Yet, there can be different consensus clusterings, depending on the choice of disagreements presented to the human expert. In particular, they may require a different number of manual inspections. We present a set of strategies to select the smallest set of manual inspections to arrive at a consensus clustering and evaluate their efficiency on a set of real-world and synthetic datasets.}, author = {Vogel, Tobias and Naumann, Felix}, booktitle = {Proceedings of the International Workshop on Data Integration and Applications}, keywords = {isg}, title = {Semi-Supervised Consensus Clustering: Reducing Human Effort}, year = 2014 }
• Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web Journal. (2014).
  [ BibTeX ]
   
  @article{Jens2014a, author = {Lehmann, Jens and Isele, Robert and Jakob, Max and Jentzsch, Anja and Kontokostas, Dimitris and Mendes, Pablo N. and Hellmann, Sebastian and Morsey, Mohamed and van Kleef, Patrick and Auer, Sören and Bizer, Christian}, journal = {Semantic Web Journal}, keywords = {isg}, note = {Selected for 2014 Semantic Web journal outstanding paper award.}, title = {DBpedia – A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia}, year = 2014 }
• Vogel, T., Heise, A., Draisbach, U., Lange, D., Naumann, F.: Reach for Gold: An Annealing Standard to Evaluate Duplicate Detection Results. JDIQ. 5, (2014).
  [ BibTeX ] [ Download ]
   
  @article{AnnealingStandard, author = {Vogel, Tobias and Heise, Arvid and Draisbach, Uwe and Lange, Dustin and Naumann, Felix}, journal = {JDIQ}, keywords = {isg}, number = {1-2}, title = {Reach for Gold: An Annealing Standard to Evaluate Duplicate Detection Results}, volume = 5, year = 2014 }
• Lorey, J.: Identifying and Determining SPARQL Endpoint Characteristics. International Journal of Web Information Systems. 10, (2014).
  [ Abstract ] [ BibTeX ]
   
  Publicly accessible SPARQL endpoints contain vast amounts of knowledge from a large variety of domains. Utilizing the structured query language, users can consume, integrate, and present data from such Linked Data sources for different application scenarios. However, oftentimes these endpoints are not configured to process specific workloads as efficiently as possible. Implemented restrictions further impede data consumption, e.g., by limiting the number of results returned per request. Assisting users in leveraging SPARQL endpoints requires insight into functional and non-functional properties of these knowledge bases. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers
  @article{Johannes2014a, abstract = {Publicly accessible SPARQL endpoints contain vast amounts of knowledge from a large variety of domains. Utilizing the structured query language, users can consume, integrate, and present data from such Linked Data sources for different application scenarios. However, oftentimes these endpoints are not configured to process specific workloads as efficiently as possible. Implemented restrictions further impede data consumption, e.g., by limiting the number of results returned per request. Assisting users in leveraging SPARQL endpoints requires insight into functional and non-functional properties of these knowledge bases. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers}, author = {Lorey, Johannes}, journal = {International Journal of Web Information Systems}, keywords = {isg}, number = 3, publisher = {Emerald Group Publishing}, title = {Identifying and Determining SPARQL Endpoint Characteristics}, volume = 10, year = 2014 }
• Krestel, R., Bergler, S., Witte, R.: Modeling human newspaper readers: The Fuzzy Believer approach. Natural Language Engineering. 20, 261--288 (2014).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our Fuzzy Believer system presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.
  @article{krestel14nlp, abstract = {The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our Fuzzy Believer system presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.}, author = {Krestel, Ralf and Bergler, Sabine and Witte, René}, journal = {Natural Language Engineering}, keywords = {news_analysis web_science isg}, month = 4, number = 2, pages = {261--288}, title = {Modeling human newspaper readers: The Fuzzy Believer approach}, volume = 20, year = 2014 }
• Alexandrov, A., Bergmann, R., Ewen, S., Freytag, J.-C., Hueske, F., Heise, A., Kao, O., Leich, M., Leser, U., Markl, V., Naumann, F., Peters, M., Rheinländer, A., Sax, M.J., Schelter, S., Höger, M., Tzoumas, K., Warneke, D.: The Stratosphere Platform for Big Data Analytics. The VLDB Journal. 23, 939-964 (2014).
  [ Abstract ] [ BibTeX ]
   
  We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.
  @article{Alexander2014a, abstract = {We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.}, author = {Alexandrov, Alexander and Bergmann, Rico and Ewen, Stephan and Freytag, Johann-Christoph and Hueske, Fabian and Heise, Arvid and Kao, Odej and Leich, Marcus and Leser, Ulf and Markl, Volker and Naumann, Felix and Peters, Mathias and Rheinländer, Astrid and Sax, Matthias J. and Schelter, Sebastian and Höger, Mareike and Tzoumas, Kostas and Warneke, Daniel}, journal = {The VLDB Journal}, keywords = {isg}, number = 6, pages = {939-964}, title = {The Stratosphere Platform for Big Data Analytics}, volume = 23, year = 2014 }

2013

• Rheinländer, A., Heise, A., Hueske, F., Leser, U., Naumann, F.: SOFA: An Extensible Logical Optimizer for UDF-heavy Dataflows. (2013).
  [ BibTeX ]
   
  @techreport{rheinlaender_sofa_2013, author = {Rheinländer, Astrid and Heise, Arvid and Hueske, Fabian and Leser, Ulf and Naumann, Felix}, journal = {CoRR}, keywords = {isg}, title = {SOFA: An Extensible Logical Optimizer for UDF-heavy Dataflows}, volume = {abs/1311.6335}, year = 2013 }
• Lorey, J., Naumann, F.: Detecting SPARQL Query Templates for Data Prefetching. Proceedings of the 10th Extended Semantic Web Conference (ESWC). , Montpellier, France (2013).
  [ Abstract ] [ BibTeX ]
   
  Publicly available Linked Data repositories provide a multitude of information. By utilizing SPARQL, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mash-ups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the SPARQL endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world SPARQL query logs.
  @inproceedings{Johannes2013a, abstract = {Publicly available Linked Data repositories provide a multitude of information. By utilizing SPARQL, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mash-ups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the SPARQL endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world SPARQL query logs.}, address = {Montpellier, France}, author = {Lorey, Johannes and Naumann, Felix}, booktitle = {Proceedings of the 10th Extended Semantic Web Conference (ESWC)}, keywords = {isg}, title = {Detecting SPARQL Query Templates for Data Prefetching}, year = 2013 }
• Lange, D., Naumann, F.: Bulk Sorted Access for Efficient Top-k Retrieval. Proceedings of the International Conference on Scientific and Statistical Database Management (SSDBM). , Baltimore, Maryland (2013).
  [ BibTeX ] [ Download ]
   
  @inproceedings{BSA_SSDBM_2013, address = {Baltimore, Maryland}, author = {Lange, Dustin and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Scientific and Statistical Database Management (SSDBM)}, keywords = {isg}, title = {Bulk Sorted Access for Efficient Top-k Retrieval}, year = 2013 }
• Lacoste-Julien, S., Palla, K., Davies, A., Kasneci, G., Graepel, T., Ghahramani, Z.: SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases. Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2013).
  [ BibTeX ]
   
  @inproceedings{Simon2013a, author = {Lacoste-Julien, Simon and Palla, Konstantina and Davies, Alex and Kasneci, Gjergji and Graepel, Thore and Ghahramani, Zoubin}, booktitle = {Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)}, keywords = {isg}, title = {SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases}, year = 2013 }
• Jenders, M., Kasneci, G., Naumann, F.: Analyzing and Predicting Viral Tweets. Proceedings of the WWW '13 Companion: 22nd International World Wide Web Conference. , Rio de Janeiro, Brazil (2013).
  [ Abstract ] [ BibTeX ]
   
  Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks. This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.
  @inproceedings{Maximilian2013a, abstract = {Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks. This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.}, address = {Rio de Janeiro, Brazil}, author = {Jenders, Maximilian and Kasneci, Gjergji and Naumann, Felix}, booktitle = {Proceedings of the WWW '13 Companion: 22nd International World Wide Web Conference}, keywords = {isg}, title = {Analyzing and Predicting Viral Tweets}, year = 2013 }
• Lorey, J., Naumann, F.: Caching and Prefetching Strategies for SPARQL Queries. ESWC 2013 Satellite Events -- Revised Selected Papers. , Montpellier, France (2013).
  [ Abstract ] [ BibTeX ]
   
  Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using SPARQL queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of only retaining results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world SPARQL query logs.
  @inproceedings{Johannes2013a, abstract = {Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using SPARQL queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of only retaining results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world SPARQL query logs.}, address = {Montpellier, France}, author = {Lorey, Johannes and Naumann, Felix}, booktitle = {ESWC 2013 Satellite Events -- Revised Selected Papers}, keywords = {isg}, title = {Caching and Prefetching Strategies for SPARQL Queries}, year = 2013 }
• Lorey, J.: Storing and Provisioning Linked Data as a Service. Proceedings of the 10th Extended Semantic Web Conference (ESWC). , Montpellier, France (2013).
  [ Abstract ] [ BibTeX ]
   
  Linked Data offers novel opportunities for aggregating information about a wide range of topics and for a multitude of applications. While the technical specifications of Linked Data have been a major research undertaking for the last decade, there is still a lack of real-world data and applications exploiting this data. Partly, this is due to the fact that datasets remain isolated from one another and their integration is a non-trivial task. In this work, we argue for a Data-as-a-Service approach combining both warehousing and query federation to discover and consume Linked Data. We compare our work to state-of-the-art approaches for discovering, integrating, and consuming Linked Data. Moreover, we illustrate a number of challenges when combining warehousing with federation features, and highlight key aspects of our research.
  @inproceedings{Johannes2013a, abstract = {Linked Data offers novel opportunities for aggregating information about a wide range of topics and for a multitude of applications. While the technical specifications of Linked Data have been a major research undertaking for the last decade, there is still a lack of real-world data and applications exploiting this data. Partly, this is due to the fact that datasets remain isolated from one another and their integration is a non-trivial task. In this work, we argue for a Data-as-a-Service approach combining both warehousing and query federation to discover and consume Linked Data. We compare our work to state-of-the-art approaches for discovering, integrating, and consuming Linked Data. Moreover, we illustrate a number of challenges when combining warehousing with federation features, and highlight key aspects of our research.}, address = {Montpellier, France}, author = {Lorey, Johannes}, booktitle = {Proceedings of the 10th Extended Semantic Web Conference (ESWC)}, keywords = {isg}, title = {Storing and Provisioning Linked Data as a Service}, year = 2013 }
• Leich, M., Adamek, J., Schubotz, M., Heise, A., Rheinlander, A., Markl, V.: Applying Stratosphere for Big Data Analytics. Database Systems for Business, Technology, and Web (BTW) (2013).
  [ BibTeX ]
   
  @inproceedings{MeteorBTW2013, author = {Leich, Marcus and Adamek, Jochen and Schubotz, Moritz and Heise, Arvid and Rheinlander, Astrid and Markl, Volker}, booktitle = {Database Systems for Business, Technology, and Web (BTW)}, keywords = {isg}, title = {Applying Stratosphere for Big Data Analytics}, year = 2013 }
• Lorey, J.: SPARQL Endpoint Metrics for Quality-Aware Linked Data Consumption. Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS '13). , Vienna, Austria (2013).
  [ Abstract ] [ BibTeX ]
   
  In recent years, dozens of publicly accessible Linked Data repositories containing vast amounts of knowledge presented in the Resource Description Framework (RDF) format have been set up worldwide. By utilizing the SPARQL query language, users can consume, integrate, and present data from a federation of sources for different application scenarios. However, several challenges arise for distributed query processing across multiple SPARQL endpoints, such as devising suitable query optimization or result caching strategies. For implementing these techniques, one crucial aspect is determining appropriate endpoint features. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers.
  @inproceedings{Johannes2013a, abstract = {In recent years, dozens of publicly accessible Linked Data repositories containing vast amounts of knowledge presented in the Resource Description Framework (RDF) format have been set up worldwide. By utilizing the SPARQL query language, users can consume, integrate, and present data from a federation of sources for different application scenarios. However, several challenges arise for distributed query processing across multiple SPARQL endpoints, such as devising suitable query optimization or result caching strategies. For implementing these techniques, one crucial aspect is determining appropriate endpoint features. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers.}, address = {Vienna, Austria}, author = {Lorey, Johannes}, booktitle = {Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS '13)}, keywords = {isg}, title = {SPARQL Endpoint Metrics for Quality-Aware Linked Data Consumption}, year = 2013 }
• Albrecht, A., Naumann, F.: Systematic ETL Management – Experiences with High-Level Operators. Proceedings of the 18th International Conference on Information Quality (ICIQ). , Little Rock, AK (2013).
  [ BibTeX ] [ Download ]
   
  @inproceedings{Albrecht13, address = {Little Rock, AK}, author = {Albrecht, Alexander and Naumann, Felix}, booktitle = {Proceedings of the 18th International Conference on Information Quality (ICIQ)}, keywords = {isg}, title = {Systematic {ETL} Management – Experiences with High-Level Operators}, year = 2013 }
• Heise, A., Quiane-Ruiz, J.-A., Abedjan, Z., Jentzsch, A., Naumann, F.: Scalable Discovery of Unique Column Combinations. Proceedings of the VLDB Endowment (PVLDB). , Hangzhou, China (2013).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  The discovery of all unique (and non-unique) column combinations in a given dataset is at the core of any data profiling effort. The results are useful for a large number of areas of data management, such as anomaly detection, data integration, data modeling, duplicate detection, indexing, and query optimization. However, discovering all unique and non-unique column combinations is an NP-hard problem, which in principle requires to verify an exponential number of column combinations for uniqueness on all data values. Thus, achieving efficiency and scalability in this context is a tremendous challenge by itself. In this paper, we devise DUCC, a scalable and efficient approach to the problem of finding all unique and non-unique column combinations in big datasets. We first model the problem as a graph coloring problem and analyze the pruning effect of individual combinations. We then present our hybrid column-based pruning technique, which traverses the lattice in a depth-first and random walk combination. This strategy allows DUCC to typically depend on the solution set size and hence to prune large swaths of the lattice. DUCC also incorporates row-based pruning to run uniqueness checks in just few milliseconds. To achieve even higher scalability, DUCC runs on several CPU cores (scale-up) and compute nodes (scale-out) with a very low overhead. We exhaustively evaluate DUCC using three datasets (two real and one synthetic) with several millions rows and hundreds of attributes. We compare DUCC with related work: Gordian and HCA. The results show that DUCC is up to more than 2 orders of magnitude faster than Gordian and HCA (631x faster than Gordian and 398x faster than HCA). Finally, a series of scalability experiments shows the efficiency of DUCC to scale up and out.
  @inproceedings{heise_duxx_2014, abstract = {The discovery of all unique (and non-unique) column combinations in a given dataset is at the core of any data profiling effort. The results are useful for a large number of areas of data management, such as anomaly detection, data integration, data modeling, duplicate detection, indexing, and query optimization. However, discovering all unique and non-unique column combinations is an NP-hard problem, which in principle requires to verify an exponential number of column combinations for uniqueness on all data values. Thus, achieving efficiency and scalability in this context is a tremendous challenge by itself. In this paper, we devise DUCC, a scalable and efficient approach to the problem of finding all unique and non-unique column combinations in big datasets. We first model the problem as a graph coloring problem and analyze the pruning effect of individual combinations. We then present our hybrid column-based pruning technique, which traverses the lattice in a depth-first and random walk combination. This strategy allows DUCC to typically depend on the solution set size and hence to prune large swaths of the lattice. DUCC also incorporates row-based pruning to run uniqueness checks in just few milliseconds. To achieve even higher scalability, DUCC runs on several CPU cores (scale-up) and compute nodes (scale-out) with a very low overhead. We exhaustively evaluate DUCC using three datasets (two real and one synthetic) with several millions rows and hundreds of attributes. We compare DUCC with related work: Gordian and HCA. The results show that DUCC is up to more than 2 orders of magnitude faster than Gordian and HCA (631x faster than Gordian and 398x faster than HCA). Finally, a series of scalability experiments shows the efficiency of DUCC to scale up and out.}, address = {Hangzhou, China}, author = {Heise, Arvid and Quiane-Ruiz, Jorge-Arnulfo and Abedjan, Ziawasch and Jentzsch, Anja and Naumann, Felix}, booktitle = {Proceedings of the VLDB Endowment (PVLDB)}, keywords = {isg}, note = {Jorge's presentation at VLDB 2014 was awarded the "Excellent Presentation Award".}, title = {Scalable Discovery of Unique Column Combinations}, year = 2013 }
• Abedjan, Z., Naumann, F.: Synonym Analysis for Predicate Expansion. Proceedings of the Extended Semantic Web Conference (ESWC), Montpellier, France (2013).
  [ BibTeX ]
   
  @inproceedings{Ziawasch2013a, author = {Abedjan, Ziawasch and Naumann, Felix}, booktitle = {Proceedings of the Extended Semantic Web Conference (ESWC), Montpellier, France}, keywords = {isg}, title = {Synonym Analysis for Predicate Expansion}, year = 2013 }
• Draisbach, U., Naumann, F.: On Choosing Thresholds for Duplicate Detection. Proceedings of the 18th International Conference on Information Quality (ICIQ). , Little Rock, USA (2013).
  [ BibTeX ] [ Download ]
   
  @inproceedings{DraisbachNaumannICIQ2013, address = {Little Rock, USA}, author = {Draisbach, Uwe and Naumann, Felix}, booktitle = {Proceedings of the 18th International Conference on Information Quality (ICIQ)}, keywords = {isg}, title = {On Choosing Thresholds for Duplicate Detection}, year = 2013 }
• Lorey, J., Naumann, F.: Caching and Prefetching Strategies for SPARQL Queries. Proceedings of the 3rd International Workshop on Usage Analysis and the Web of Data (USEWOD). , Montpellier, France (2013).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using SPARQL queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of only retaining results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world SPARQL query logs.
  @inproceedings{usewod2013, abstract = {Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using SPARQL queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of only retaining results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world SPARQL query logs.}, address = {Montpellier, France}, author = {Lorey, Johannes and Naumann, Felix}, booktitle = {Proceedings of the 3rd International Workshop on Usage Analysis and the Web of Data (USEWOD)}, keywords = {isg}, note = {Selected as Best Workshop Paper for publication in ESWC post-proceedings}, title = {Caching and Prefetching Strategies for SPARQL Queries}, year = 2013 }
• Forchhammer, B., Papenbrock, T., Stening, T., Viehmeier, S., Draisbach, U., Naumann, F.: Duplicate Detection on GPUs. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW). pp. 165-184 (2013).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  With the ever increasing volume of data and the ability to integrate different data sources, data quality problems abound. Duplicate detection, as an integral part of data cleansing, is essential in modern information systems. We present a complete duplicate detection workflow that utilizes the capabilities of modern graphics processing units (GPUs) to increase the efficiency of finding duplicates in very large datasets. Our solution covers several well-known algorithms for pair selection, attribute-wise similarity comparison, record-wise similarity aggregation, and clustering. We redesigned these algorithms to run memory-efficiently and in parallel on the GPU. Our experiments demonstrate that the GPU-based workflow is able to outperform a CPU-based implementation on large, real-world datasets. For instance, the GPU-based algorithm deduplicates a dataset with 1.8m entities 10 times faster than a common CPU-based algorithm using comparably priced hardware.
  @inproceedings{duplicate2013forchhammer, abstract = {With the ever increasing volume of data and the ability to integrate different data sources, data quality problems abound. Duplicate detection, as an integral part of data cleansing, is essential in modern information systems. We present a complete duplicate detection workflow that utilizes the capabilities of modern graphics processing units (GPUs) to increase the efficiency of finding duplicates in very large datasets. Our solution covers several well-known algorithms for pair selection, attribute-wise similarity comparison, record-wise similarity aggregation, and clustering. We redesigned these algorithms to run memory-efficiently and in parallel on the GPU. Our experiments demonstrate that the GPU-based workflow is able to outperform a CPU-based implementation on large, real-world datasets. For instance, the GPU-based algorithm deduplicates a dataset with 1.8m entities 10 times faster than a common CPU-based algorithm using comparably priced hardware.}, author = {Forchhammer, Benedikt and Papenbrock, Thorsten and Stening, Thomas and Viehmeier, Sven and Draisbach, Uwe and Naumann, Felix}, booktitle = {Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW)}, keywords = {duplicate_detection parallel data_cleansing gpu isg}, note = {Runner Up for Best Paper Award}, pages = {165-184}, title = {Duplicate Detection on GPUs}, year = 2013 }
• Momtazi, S., Naumann, F.: Topic modeling for expert finding using latent dirichlet allocation. WIREs Data Mining and Knowledge Discovery. 3, 346–353 (2013).
  [ Abstract ] [ BibTeX ]
   
  The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic-based approach outperforms the state-of-the-art profile- and document-based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic-based approach to the improved document-based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.
  @article{Saeedeh2013a, abstract = {The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic-based approach outperforms the state-of-the-art profile- and document-based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic-based approach to the improved document-based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.}, author = {Momtazi, Saeedeh and Naumann, Felix}, journal = {WIREs Data Mining and Knowledge Discovery}, keywords = {isg}, number = 5, pages = {346–353}, title = {Topic modeling for expert finding using latent dirichlet allocation}, volume = 3, year = 2013 }
• Abedjan, Z., Naumann, F.: Improving RDF Data through Association Rule Mining. Datenbank-Spektrum (Special Issue on RDF Data Management). 13, 111--120 (2013).
  [ BibTeX ]
   
  @article{abedjan_naumann_dbs_2013, author = {Abedjan, Ziawasch and Naumann, Felix}, edition = {Special Issue on RDF Data Management}, journal = {Datenbank-Spektrum (Special Issue on RDF Data Management)}, keywords = {isg}, number = 2, pages = {111--120}, publisher = {Springer}, series = {Special Issue on RDF Data Management}, title = {Improving RDF Data through Association Rule Mining}, volume = 13, year = 2013 }
• Naumann, F., Jenders, M., Papenbrock, T.: Ein Datenbankkurs mit 6000 Teilnehmern - Erfahrungen auf der openHPI MOOC Plattform. Informatik-Spektrum. 37, 333-340 (2013).
  [ BibTeX ] [ URL ]
   
  @article{Felix2013a, author = {Naumann, Felix and Jenders, Maximilian and Papenbrock, Thorsten}, journal = {Informatik-Spektrum}, keywords = {isg}, number = 12, pages = {333-340}, title = {Ein Datenbankkurs mit 6000 Teilnehmern - Erfahrungen auf der openHPI MOOC Plattform}, volume = 37, year = 2013 }
• Lange, D., Naumann, F.: Cost-Aware Query Planning for Similarity Search. Information Systems (IS). 38, 455--469 (2013).
  [ BibTeX ] [ Download ]
   
  @article{QueryPlanningSimilaritySearch2012, author = {Lange, Dustin and Naumann, Felix}, journal = {Information Systems (IS)}, keywords = {isg}, number = 4, pages = {455--469}, title = {Cost-Aware Query Planning for Similarity Search}, volume = 38, year = 2013 }
• Naumann, F.: Data Profiling Revisited. SIGMOD Record. 32, 40-49 (2013).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies. Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, more and more data beyond the traditional relational databases are being created and beg to be profiled. The article proposes new research directions and challenges, including interactive and incremental profiling and profiling heterogeneous and non-relational data.
  @article{Nau13, abstract = {Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies. Data profiling deserves a fresh look for two reasons: First, the area itself is neither established nor defined in any principled way, despite significant research activity on individual parts in the past. Second, more and more data beyond the traditional relational databases are being created and beg to be profiled. The article proposes new research directions and challenges, including interactive and incremental profiling and profiling heterogeneous and non-relational data.}, author = {Naumann, Felix}, journal = {SIGMOD Record}, keywords = {isg}, number = 4, pages = {40-49}, title = {Data Profiling Revisited}, volume = 32, year = 2013 }
• Rinser, D., Lange, D., Naumann, F.: Cross-lingual Entity Matching and Infobox Alignment in Wikipedia. Information Systems (IS). 38, 887–907 (2013).
  [ BibTeX ]
   
  @article{WikipediaEntityMatching2012, author = {Rinser, Daniel and Lange, Dustin and Naumann, Felix}, journal = {Information Systems (IS)}, keywords = {isg}, number = 6, pages = {887–907}, title = {Cross-lingual Entity Matching and Infobox Alignment in Wikipedia}, volume = 38, year = 2013 }

2012

• Draisbach, U., Naumann, F.: Adaptive Windows for Duplicate Detection. Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam (2012).
  [ BibTeX ] [ Download ]
   
  @techreport{DraisbachNaumann2012AdaptiveWindowsTR49, author = {Draisbach, Uwe and Naumann, Felix}, institution = {Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam}, keywords = {isg}, note = {ISBN 978-3-86956-143-1, ISSN 1613-5652}, number = 49, title = {Adaptive Windows for Duplicate Detection}, year = 2012 }
• Bauckmann, J., Abedjan, Z., Leser, U., Müller, H., Naumann, F.: Covering or complete? : discovering conditional inclusion dependencies. Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam (2012).
  [ BibTeX ]
   
  @techreport{cind_tech_report_2012, author = {Bauckmann, Jana and Abedjan, Ziawasch and Leser, Ulf and Müller, Heiko and Naumann, Felix}, institution = {Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam}, keywords = {isg}, note = {ISBN 978-3-86956-212-4, ISSN 1613-5652}, number = 62, title = {Covering or complete? : discovering conditional inclusion dependencies}, year = 2012 }
• Albrecht, A., Naumann, F.: Understanding Cryptic Schemata in Large Extract-Transform-Load Systems. Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam (2012).
  [ BibTeX ]
   
  @techreport{decryption_tech_report_2012, author = {Albrecht, Alexander and Naumann, Felix}, institution = {Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam}, keywords = {isg}, note = {ISBN 978-3-86956-201-8, ISSN 1613-5652}, number = 60, title = {Understanding Cryptic Schemata in Large Extract-Transform-Load Systems}, year = 2012 }
• Albrecht, A., Naumann, F.: Schema Decryption for Large Extract-Transform-Load Systems. Proceedings of the 31st International Conference on Conceptual Modeling (ER 2012). , Florence, Italy (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{ER2012Albrecht, address = {Florence, Italy}, author = {Albrecht, Alexander and Naumann, Felix}, booktitle = {Proceedings of the 31st International Conference on Conceptual Modeling (ER 2012)}, keywords = {isg}, title = {Schema Decryption for Large Extract-Transform-Load Systems}, year = 2012 }
• Tafaj, E., Kasneci, G., Rosenstiel, W., Bogdan, M.: Bayesian online clustering of eye movement data. Proceedings of the 2012 Symposium on Eye-Tracking Research and Applications. pp. 285-288. ACM (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{TafajKRB12, author = {Tafaj, Enkelejda and Kasneci, Gjergji and Rosenstiel, Wolfgang and Bogdan, Martin}, booktitle = {Proceedings of the 2012 Symposium on Eye-Tracking Research and Applications}, keywords = {isg}, pages = {285-288}, publisher = {ACM}, title = {Bayesian online clustering of eye movement data}, year = 2012 }
• Böhm, C., Kasneci, G., Naumann, F.: Latent Topics in Graph-Structured Data. Proceedings of the Conference on Information and Knowledge Management (CIKM) (2012).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Large amounts of graph-structured data are emerging from various avenues, ranging from natural and life sciences to so- cial and semantic web communities. We address the problem of discovering subgraphs of entities that reflect latent topics in graph-structured data. These topics are structured meta- information providing further insights into the data. The presented approach effectively detects such topics by exploit- ing only the structure of the underlying graph, thus avoiding the dependency on textual labels, which are a scarce asset in prevalent graph datasets. The viability of our approach is demonstrated in experiments on real-world datasets.
  @inproceedings{boehm-topics-2012, abstract = {Large amounts of graph-structured data are emerging from various avenues, ranging from natural and life sciences to so- cial and semantic web communities. We address the problem of discovering subgraphs of entities that reflect latent topics in graph-structured data. These topics are structured meta- information providing further insights into the data. The presented approach effectively detects such topics by exploit- ing only the structure of the underlying graph, thus avoiding the dependency on textual labels, which are a scarce asset in prevalent graph datasets. The viability of our approach is demonstrated in experiments on real-world datasets.}, author = {Böhm, Christoph and Kasneci, Gjergji and Naumann, Felix}, booktitle = {Proceedings of the Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, title = {Latent Topics in Graph-Structured Data}, year = 2012 }
• Gruetze, T., Böhm, C., Naumann, F.: Holistic and Scalable Ontology Alignment for Linked Open Data. Proceedings of the 5th Linked Data on the Web (LDOW) Workshop at the 21th International World Wide Web Conference (WWW). , Lyon, France (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{www_GruetzeBN2012, address = {Lyon, France}, author = {Gruetze, Toni and Böhm, Christoph and Naumann, Felix}, booktitle = {Proceedings of the 5th Linked Data on the Web (LDOW) Workshop at the 21th International World Wide Web Conference (WWW)}, keywords = {isg}, month = 4, title = {Holistic and Scalable Ontology Alignment for Linked Open Data}, year = 2012 }
• Vogel, T., Naumann, F.: Automatic Blocking Key Selection for Duplicate Detection based on Unigram Combinations. Proceedings of the 10th International Workshop on Quality in Databases (QDB) in conjunction with VLDB (2012).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Duplicate detection is the process of identifying multiple but different representations of same real-world objects, which typically involves a large number of comparisons. Partitioning is a well-known technique to avoid many unnecessary comparisons. However, partitioning keys are usually handcrafted, which is tedious and the keys are often poorly chosen. We propose a technique to find suitable blocking keys automatically for a dataset equipped with a gold standard. We then show how to re-use those blocking keys for datasets from similar domains lacking a gold standard. Blocking keys are created based on unigrams, which we extend with length-hints for further improvement. Blocking key creation is accompanied with several comprehensive experiments on large artificial and real-world datasets.
  @inproceedings{UnigramblockingVogelNaumann2012, abstract = {Duplicate detection is the process of identifying multiple but different representations of same real-world objects, which typically involves a large number of comparisons. Partitioning is a well-known technique to avoid many unnecessary comparisons. However, partitioning keys are usually handcrafted, which is tedious and the keys are often poorly chosen. We propose a technique to find suitable blocking keys automatically for a dataset equipped with a gold standard. We then show how to re-use those blocking keys for datasets from similar domains lacking a gold standard. Blocking keys are created based on unigrams, which we extend with length-hints for further improvement. Blocking key creation is accompanied with several comprehensive experiments on large artificial and real-world datasets.}, author = {Vogel, Tobias and Naumann, Felix}, booktitle = {Proceedings of the 10th International Workshop on Quality in Databases (QDB) in conjunction with VLDB}, keywords = {isg}, title = {Automatic Blocking Key Selection for Duplicate Detection based on Unigram Combinations}, year = 2012 }
• Böhm, C., Freitag, M., Heise, A., Lehmann, C., Mascher, A., Naumann, F., Hernandez, M., Ercegovac, V., Haase, P.: GovWILD: Integrating Open Government Data for Transparency (demo). Proceedings of the International World Wide Web Conference (WWW). , Lyon, France (2012).
  [ BibTeX ]
   
  @inproceedings{Christoph2012a, address = {Lyon, France}, author = {Böhm, Christoph and Freitag, Markus and Heise, Arvid and Lehmann, Claudia and Mascher, Andrina and Naumann, Felix and Hernandez, Mauricio and Ercegovac, Vuk and Haase, Peter}, booktitle = {Proceedings of the International World Wide Web Conference (WWW)}, keywords = {isg}, title = {GovWILD: Integrating Open Government Data for Transparency (demo)}, year = 2012 }
• Köppelmann, M., Lange, D., Lehmann, C., Marszalkowski, M., Naumann, F., Retzlaff, P., Stange, S., Voget, L.: Scalable Similarity Search with Dynamic Similarity Measures. Proceedings of the 6th International Workshop on Ranking in Databases (DBRank) in conjunction with VLDB. , Istanbul, Turkey (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{DBRank_LuSim_2012, address = {Istanbul, Turkey}, author = {Köppelmann, Martin and Lange, Dustin and Lehmann, Claudia and Marszalkowski, Marika and Naumann, Felix and Retzlaff, Peter and Stange, Sebastian and Voget, Lea}, booktitle = {Proceedings of the 6th International Workshop on Ranking in Databases (DBRank) in conjunction with VLDB}, keywords = {isg}, title = {Scalable Similarity Search with Dynamic Similarity Measures}, year = 2012 }
• Momtazi, S.: Fine-grained German Sentiment Analysis on Social Media. Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC). , Istanbul, Turkey (2012).
  [ BibTeX ]
   
  @inproceedings{LREC_SentimentAnalysis_2012, address = {Istanbul, Turkey}, author = {Momtazi, Saeedeh}, booktitle = {Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC)}, keywords = {isg}, title = {Fine-grained German Sentiment Analysis on Social Media}, year = 2012 }
• Abedjan, Z., Lorey, J., Naumann, F.: Reconciling Ontologies and the Web of Data. Proceedings of the 21st International Conference on Information and Knowledge Management (CIKM). pp. 1532-1536. , Maui, Hawaii, USA (2012).
  [ BibTeX ]
   
  @inproceedings{Ziawasch2012a, address = {Maui, Hawaii, USA}, author = {Abedjan, Ziawasch and Lorey, Johannes and Naumann, Felix}, booktitle = {Proceedings of the 21st International Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {1532-1536}, title = {Reconciling Ontologies and the Web of Data}, year = 2012 }
• Böhm, C., de Melo, G., Naumann, F., Weikum, G.: LINDA: Distributed Web-of-Data-Scale Entity Matching. Proceedings of the International Conference on Information and Knowledge Management (CIKM), Maui, Hawaii (2012).
  [ BibTeX ]
   
  @inproceedings{Christoph2012a, author = {Böhm, Christoph and de Melo, Gerard and Naumann, Felix and Weikum, Gerhard}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM), Maui, Hawaii}, keywords = {isg}, title = {LINDA: Distributed Web-of-Data-Scale Entity Matching}, year = 2012 }
• Draisbach, U., Naumann, F., Szott, S., Wonneberg, O.: Adaptive Windows for Duplicate Detection. Proceedings of the 28th International Conference on Data Engineering (ICDE). , Washington, D.C., USA (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_ICDE_DraisbachNSW_2012, address = {Washington, D.C., USA}, author = {Draisbach, Uwe and Naumann, Felix and Szott, Sascha and Wonneberg, Oliver}, booktitle = {Proceedings of the 28th International Conference on Data Engineering (ICDE)}, keywords = {isg}, title = {Adaptive Windows for Duplicate Detection}, year = 2012 }
• Heise, A., Rheinländer, A., Leich, M., Leser, U., Naumann, F.: Meteor/Sopremo: An Extensible Query Language and Operator Model. Proceedings of the International Workshop on End-to-end Management of Big Data (BigData) in conjunction with VLDB 2012. , Istanbul, Turkey (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{MeteorSopremo_BigData_2012, address = {Istanbul, Turkey}, author = {Heise, Arvid and Rheinländer, Astrid and Leich, Marcus and Leser, Ulf and Naumann, Felix}, booktitle = {Proceedings of the International Workshop on End-to-end Management of Big Data (BigData) in conjunction with VLDB 2012}, keywords = {isg}, title = {Meteor/Sopremo: An Extensible Query Language and Operator Model}, year = 2012 }
• Fenz, D., Lange, D., Rheinländer, A., Naumann, F., Leser, U.: Efficient Similarity Search in Very Large String Sets. Proceedings of the International Conference on Scientific and Statistical DatabaseManagement (SSDBM). , Chania, Crete, Greece (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{StateSetIndex_SSDBM_2012, address = {Chania, Crete, Greece}, author = {Fenz, Dandy and Lange, Dustin and Rheinländer, Astrid and Naumann, Felix and Leser, Ulf}, booktitle = {Proceedings of the International Conference on Scientific and Statistical DatabaseManagement (SSDBM)}, keywords = {isg}, title = {Efficient Similarity Search in Very Large String Sets}, year = 2012 }
• Böhm, C., Hefenbrock, D., Naumann, F.: Scalable Peer-to-Peer-based RDF Management. Proceedings of the 8th Int. Conference on Semantic Systems. , Graz, Austria (2012).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Handling web-scale RDF data requires sophisticated data management that scales easily and integrates seamlessly into existing analysis workflows. We present HDRS – a scalable storage infrastructure that enables online-analysis of very large RDF data sets. Hdrs combines state-of-the-art data management techniques to organize triples in indexes that are sharded and stored in a peer-to-peer system. The store is open source at http://code.google.com/p/hdrs and integrates well with Hadoop MapReduce or any other client application.
  @inproceedings{boehmisem2012, abstract = {Handling web-scale RDF data requires sophisticated data management that scales easily and integrates seamlessly into existing analysis workflows. We present HDRS – a scalable storage infrastructure that enables online-analysis of very large RDF data sets. Hdrs combines state-of-the-art data management techniques to organize triples in indexes that are sharded and stored in a peer-to-peer system. The store is open source at http://code.google.com/p/hdrs and integrates well with Hadoop MapReduce or any other client application.}, address = {Graz, Austria}, author = {Böhm, Christoph and Hefenbrock, Daniel and Naumann, Felix}, booktitle = {Proceedings of the 8th Int. Conference on Semantic Systems}, keywords = {isg}, month = 9, title = {Scalable Peer-to-Peer-based RDF Management}, year = 2012 }
• Bauckmann, J., Abedjan, Z., Müller, H., Leser, U., Naumann, F.: Discovering Conditional Inclusion Dependencies. Proceedings of the International Conference on Information and Knowledge Management (CIKM), Maui, Hawaii. pp. 2094-2098 (2012).
  [ BibTeX ]
   
  @inproceedings{Jana2012a, author = {Bauckmann, Jana and Abedjan, Ziawasch and Müller, Heiko and Leser, Ulf and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM), Maui, Hawaii}, keywords = {isg}, pages = {2094-2098}, title = {Discovering Conditional Inclusion Dependencies}, year = 2012 }
• Kasneci, G.: Reasoning about Knowledge from the Web - (Extended Abstract). ICWE Workshops. pp. 186-188. Springer (2012).
  [ BibTeX ] [ Download ]
   
  @inproceedings{icwe-Kasneci12, author = {Kasneci, Gjergji}, booktitle = {ICWE Workshops}, keywords = {isg}, pages = {186-188}, publisher = {Springer}, title = {Reasoning about Knowledge from the Web - (Extended Abstract)}, year = 2012 }
• Draisbach, U.: Partitionierung zur effizienten Duplikaterkennung in relationalen Daten. Springer Vieweg (2012).
  [ BibTeX ]
   
  @book{Draisbach2012, author = {Draisbach, Uwe}, keywords = {isg}, publisher = {Springer Vieweg}, series = {Ausgezeichnete Arbeiten zur Informationsqualität}, title = {Partitionierung zur effizienten Duplikaterkennung in relationalen Daten}, year = 2012 }
• Beskales, G., Das, G., Elmagarmid, A.K., Ilyas, I.F., Naumann, F., Ouzzani, M., Papotti, P., Quiane-Ruiz, J., Tang, N.: The Data Analytics Group at the Qatar Computing Research Institute. SIGMOD Record. 41, (2012).
  [ BibTeX ]
   
  @article{George2012a, author = {Beskales, George and Das, Gautam and Elmagarmid, Ahmed K. and Ilyas, Ihab F. and Naumann, Felix and Ouzzani, Mourad and Papotti, Paolo and Quiane-Ruiz, Jorge and Tang, Nan}, journal = {SIGMOD Record}, keywords = {isg}, number = 4, title = {The Data Analytics Group at the Qatar Computing Research Institute}, volume = 41, year = 2012 }
• Herschel, M., Naumann, F., Szott, S., Taubert, M.: Scalable Iterative Graph Duplicate Detection. Transactions on Knowledge and Data Engineering (TKDE). 24, 2094-2108 (2012).
  [ BibTeX ]
   
  @article{Melanie2012a, author = {Herschel, Melanie and Naumann, Felix and Szott, Sascha and Taubert, Maik}, journal = {Transactions on Knowledge and Data Engineering (TKDE)}, keywords = {isg}, number = 11, pages = {2094-2108}, title = {Scalable Iterative Graph Duplicate Detection}, volume = 24, year = 2012 }
• Heise, A., Naumann, F.: Integrating Open Government Data with Stratosphere for more Transparency. Web Semantics: Science, Services and Agents on the World Wide Web. 14, 45 - 56 (2012).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Governments are increasingly publishing their data to enable organizations and citizens to browse and analyze thedata. However, the heterogeneity of this Open Government Data hinders meaningful search, analysis, and integrationand thus limits the desired transparency.In this article, we present the newly developed data integration operators of the Stratosphere parallel data analysisframework to overcome the heterogeneity. With declaratively specified queries, we demonstrate the integration ofwell-known government data sources and other large open data sets at technical, structural, and semantic levels.Furthermore, we publish the integrated data on theWeb in a form that enables users to discover relationships betweenpersons, government agencies, funds, and companies. The evaluation shows that linking person entities of dierentdata sets results in a good precision of 98.3% and a recall of 95.2%. Moreover, the integration of large data sets scaleswell on up to eight machines.
  @article{HeiseNaumann2012, abstract = {Governments are increasingly publishing their data to enable organizations and citizens to browse and analyze thedata. However, the heterogeneity of this Open Government Data hinders meaningful search, analysis, and integrationand thus limits the desired transparency.In this article, we present the newly developed data integration operators of the Stratosphere parallel data analysisframework to overcome the heterogeneity. With declaratively specified queries, we demonstrate the integration ofwell-known government data sources and other large open data sets at technical, structural, and semantic levels.Furthermore, we publish the integrated data on theWeb in a form that enables users to discover relationships betweenpersons, government agencies, funds, and companies. The evaluation shows that linking person entities of dierentdata sets results in a good precision of 98.3% and a recall of 95.2%. Moreover, the integration of large data sets scaleswell on up to eight machines.}, author = {Heise, Arvid and Naumann, Felix}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, keywords = {map-reduce Data_integration data_cleansing parallel_query_processing record_linkage data_fusion isg schema_mapping}, number = 1, pages = {45 - 56}, publisher = {Elsevier}, title = {Integrating Open Government Data with Stratosphere for more Transparency}, volume = 14, year = 2012 }
• Abelló, A., Darmont, J., Etcheverry, L., Golfarelli, M., Mazón, J.-N., Naumann, F., Pedersen, T.B., Rizzi, S., Trujillo, J., Vassiliadis, P., Vossen, G.: Fusion Cubes: Towards Self-Service Business Intelligence. International Journal of Data Warehousing and Mining (IJDWM). 9, 66-88 (2012).
  [ Abstract ] [ BibTeX ]
   
  Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker, their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges, the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.
  @article{FusionCubes12, abstract = {Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker, their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges, the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.}, author = {Abelló, Alberto and Darmont, Jérôme and Etcheverry, Lorena and Golfarelli, Matteo and Mazón, Jose-Norberto and Naumann, Felix and Pedersen, Torben Bach and Rizzi, Stefano and Trujillo, Juan and Vassiliadis, Panos and Vossen, Gottfried}, journal = {International Journal of Data Warehousing and Mining (IJDWM)}, keywords = {isg}, number = 2, pages = {66-88}, title = {Fusion Cubes: Towards Self-Service Business Intelligence}, volume = 9, year = 2012 }

2011

• Abedjan, Z., Naumann, F.: Advancing the Discovery of Unique Column Combinations. Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam (2011).
  [ BibTeX ]
   
  @techreport{unique_techreport_2011, author = {Abedjan, Ziawasch and Naumann, Felix}, institution = {Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam}, keywords = {isg}, note = {ISBN 978-3-86956-148-6, ISSN 1613-5652}, number = 51, title = {Advancing the Discovery of Unique Column Combinations}, year = 2011 }
• Lorey, J., Naumann, F., Forchhammer, B., Mascher, A., Retzlaff, P., ZamaniFarahani, A., Discher, S., Faehnrich, C., Lemme, S., Papenbrock, T., Peschel, R.C., Richter, S., Stening, T., Viehmeier, S.: Black Swan: Augmenting Statistics with Event Data. Proceedings of the 20th Conference on Information and Knowledge Management (CIKM). pp. 2517-2520. , Glasgow, UK (2011).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  A large number of statistical indicators (GDP, life expectancy, income, etc.) collected over long periods of time as well as data on historical events (wars, earthquakes, elections, etc.) are published on the World Wide Web. By augmenting statistical outliers with relevant historical occurrences, we provide a means to observe (and predict) the influence and impact of events. The vast amount and size of available data sets enable the detection of recurring connections between classes of events and statistical outliers with the help of association rule mining. The results of this analysis are published at http://www.blackswanevents.org and can be explored interactively.
  @inproceedings{blackswan, abstract = {A large number of statistical indicators (GDP, life expectancy, income, etc.) collected over long periods of time as well as data on historical events (wars, earthquakes, elections, etc.) are published on the World Wide Web. By augmenting statistical outliers with relevant historical occurrences, we provide a means to observe (and predict) the influence and impact of events. The vast amount and size of available data sets enable the detection of recurring connections between classes of events and statistical outliers with the help of association rule mining. The results of this analysis are published at http://www.blackswanevents.org and can be explored interactively.}, address = {Glasgow, UK}, author = {Lorey, Johannes and Naumann, Felix and Forchhammer, Benedikt and Mascher, Andrina and Retzlaff, Peter and ZamaniFarahani, Armin and Discher, Soeren and Faehnrich, Cindy and Lemme, Stefan and Papenbrock, Thorsten and Peschel, Robert Christoph and Richter, Stephan and Stening, Thomas and Viehmeier, Sven}, booktitle = {Proceedings of the 20th Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {2517-2520}, title = {Black Swan: Augmenting Statistics with Event Data}, year = 2011 }
• Vogel, T., Naumann, F.: Instance-based "one-to-some" Assignment of Similarity Measures to Attributes. Proceedings of the 19th International Conference on Cooperative Information Systems (CoopIS) (2011).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Data quality is a key factor for economical success. It is usually defined as a set of properties of data, such as completeness, accessibility, relevance, and conciseness. The latter includes the absence of multiple representations for same real world objects. To avoid such duplicates, there is a wide range of commercial products and customized self-coded software. These programs can be quite expensive both in acquisition and maintenance. In particular, small and medium-sized companies cannot afford these tools. Moreover, it is difficult to set up and tune all necessary parameters in these programs. Recently, web-based applications for duplicate detection have emerged. However, they are not easy to integrate into the local IT landscape and require much manual configuration effort. With DAQS (Data Quality as a Service) we present a novel approach to support duplicate detection. The approach features (1) minimal required user interaction and (2) self-configuration for the provided input data. To this end, each data cleansing task is classified to find out which metadata is available. Next, similarity measures are automatically assigned to the provided records’ attributes and a duplicate detection process is carried out. In this paper we introduce a novel matching approach, called one-to-some or 1:k assignment, to assign similarity measures to attributes. We performed an extensive evaluation on a large training corpus and ten test datasets of address data and achieved promising results.
  @inproceedings{Instancebasedonetosomeassignmentofsimilaritymeasurestoattributescoopis2011vogelnaumann, abstract = {Data quality is a key factor for economical success. It is usually defined as a set of properties of data, such as completeness, accessibility, relevance, and conciseness. The latter includes the absence of multiple representations for same real world objects. To avoid such duplicates, there is a wide range of commercial products and customized self-coded software. These programs can be quite expensive both in acquisition and maintenance. In particular, small and medium-sized companies cannot afford these tools. Moreover, it is difficult to set up and tune all necessary parameters in these programs. Recently, web-based applications for duplicate detection have emerged. However, they are not easy to integrate into the local IT landscape and require much manual configuration effort. With DAQS (Data Quality as a Service) we present a novel approach to support duplicate detection. The approach features (1) minimal required user interaction and (2) self-configuration for the provided input data. To this end, each data cleansing task is classified to find out which metadata is available. Next, similarity measures are automatically assigned to the provided records’ attributes and a duplicate detection process is carried out. In this paper we introduce a novel matching approach, called one-to-some or 1:k assignment, to assign similarity measures to attributes. We performed an extensive evaluation on a large training corpus and ten test datasets of address data and achieved promising results.}, author = {Vogel, Tobias and Naumann, Felix}, booktitle = {Proceedings of the 19th International Conference on Cooperative Information Systems (CoopIS)}, keywords = {isg}, title = {Instance-based "one-to-some" Assignment of Similarity Measures to Attributes}, year = 2011 }
• Lange, D., Naumann, F.: Efficient Similarity Search: Arbitrary Similarity Measures, Arbitrary Composition. Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM). p. 1679--1688. , Glasgow, Scotland, UK (2011).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_cikm_SimSearchComposed_LangeN2011, address = {Glasgow, Scotland, UK}, author = {Lange, Dustin and Naumann, Felix}, booktitle = {Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {1679--1688}, title = {Efficient Similarity Search: Arbitrary Similarity Measures, Arbitrary Composition}, year = 2011 }
• Lange, D., Naumann, F.: Frequency-aware Similarity Measures. Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM). p. 243--248. , Glasgow, Scotland, UK (2011).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_cikm_FreqSim_LangeN2011, address = {Glasgow, Scotland, UK}, author = {Lange, Dustin and Naumann, Felix}, booktitle = {Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {243--248}, title = {Frequency-aware Similarity Measures}, year = 2011 }
• AbuJarour, M., Naumann, F.: Improving Service Discovery through Enriched Service Descriptions. Datenbanksysteme für Business, Technologie und Web (BTW), 14. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2.-4.3.2011 in Kaiserslautern, Germany. pp. 706-709 (2011).
  [ BibTeX ]
   
  @inproceedings{conf_btw_AbuJarourN09, author = {AbuJarour, Mohammed and Naumann, Felix}, booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW), 14. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2.-4.3.2011 in Kaiserslautern, Germany}, keywords = {isg}, pages = {706-709}, title = {Improving Service Discovery through Enriched Service Descriptions}, year = 2011 }
• Abedjan, Z., Naumann, F.: Context and Target Configurations for Mining RDF Data. International Workshop on Search & Mining Entity-Relationship Data (SMER), Glasgow, UK (2011).
  [ BibTeX ]
   
  @inproceedings{abedjan_rdf_mining_2011, author = {Abedjan, Ziawasch and Naumann, Felix}, booktitle = {International Workshop on Search & Mining Entity-Relationship Data (SMER), Glasgow, UK}, keywords = {isg}, title = {Context and Target Configurations for Mining RDF Data}, year = 2011 }
• Draisbach, U., Naumann, F.: A Generalization of Blocking and Windowing Algorithms for Duplicate Detection. Proceedings of the International Conference on Data and Knowledge Engineering (ICDKE). , Milan, Italy (2011).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Duplicate detection is the process of finding multiple records in a dataset that represent the same real-world entity. Due to the enormous costs of an exhaustive comparison, typical algorithms select only promising record pairs for comparison. Two competing approaches are blocking and windowing. Blocking methods partition records into disjoint subsets, while windowing methods, in particular the Sorted Neighborhood Method, slide a window over the sorted records and compare records only within the window. We present a new algorithm called Sorted Blocks in several variants, which generalizes both approaches. To evaluate Sorted Blocks, we have conducted extensive experiments with different datasets. These show that our new algorithm needs fewer comparisons to find the same number of duplicates.
  @inproceedings{conf_ICDKE_DraisbachN_2011, abstract = {Duplicate detection is the process of finding multiple records in a dataset that represent the same real-world entity. Due to the enormous costs of an exhaustive comparison, typical algorithms select only promising record pairs for comparison. Two competing approaches are blocking and windowing. Blocking methods partition records into disjoint subsets, while windowing methods, in particular the Sorted Neighborhood Method, slide a window over the sorted records and compare records only within the window. We present a new algorithm called Sorted Blocks in several variants, which generalizes both approaches. To evaluate Sorted Blocks, we have conducted extensive experiments with different datasets. These show that our new algorithm needs fewer comparisons to find the same number of duplicates.}, address = {Milan, Italy}, author = {Draisbach, Uwe and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Data and Knowledge Engineering (ICDKE)}, keywords = {isg}, title = {A Generalization of Blocking and Windowing Algorithms for Duplicate Detection}, year = 2011 }
• Böhm, C., Kny, E., Emde, B., Abedjan, Z., Naumann, F.: SPRINT: ranking search results by paths. International Conference on Extending Database Technology (EDBT), Uppsala, Sweden. pp. 546-549 (2011).
  [ BibTeX ] [ URL ]
   
  @inproceedings{conf_edbt_BohmKEAN11, author = {Böhm, Christoph and Kny, Eyk and Emde, Benjamin and Abedjan, Ziawasch and Naumann, Felix}, booktitle = {International Conference on Extending Database Technology (EDBT), Uppsala, Sweden}, keywords = {isg}, pages = {546-549}, title = {SPRINT: ranking search results by paths}, year = 2011 }
• Lorey, J., Abedjan, Z., Naumann, F., Böhm, C.: RDF Ontology (Re-)Engineering through Large-scale Data Mining. Billion Triples Challenge (BTC) at the 10th International Semantic Web Conference (ISWC). , Koblenz, Germany (2011).
  [ BibTeX ] [ Download ]
   
  @inproceedings{btc2011, address = {Koblenz, Germany}, author = {Lorey, Johannes and Abedjan, Ziawasch and Naumann, Felix and Böhm, Christoph}, booktitle = {Billion Triples Challenge (BTC) at the 10th International Semantic Web Conference (ISWC)}, keywords = {isg}, note = {Finalist}, title = {RDF Ontology (Re-)Engineering through Large-scale Data Mining}, year = 2011 }
• Abedjan, Z., Naumann, F.: Advancing the Discovery of Unique Column Combinations. Proceedings of the International Conference on Information and Knowledge Management (CIKM), Glasgow, UK (2011).
  [ BibTeX ]
   
  @inproceedings{unique_discovery_za_fn_2011, author = {Abedjan, Ziawasch and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM), Glasgow, UK}, keywords = {isg}, title = {Advancing the Discovery of Unique Column Combinations}, year = 2011 }
• Böhm, C., Lorey, J., Naumann, F.: Creating voiD Descriptions for Web-scale Data. Journal of Web Semantics: Science, Services and Agents on the World Wide Web. 9, 339-345 (2011).
  [ BibTeX ] [ Download ]
   
  @article{voidJWS, author = {Böhm, Christoph and Lorey, Johannes and Naumann, Felix}, journal = {Journal of Web Semantics: Science, Services and Agents on the World Wide Web}, keywords = {Cloud_Computing RDF_Metadata_Generation Semantic_Web Vocabulary_of_Interlinked_Data isg Semantic_Data_Profiling}, number = 3, pages = {339-345}, title = {Creating voiD Descriptions for Web-scale Data}, volume = 9, year = 2011 }
• Lange, D., Vogel, T., Draisbach, U., Naumann, F.: Projektseminar "Similarity Search Algorithms". Datenbank-Spektrum. 11, 51-57 (2011).
  [ BibTeX ] [ Download ]
   
  @article{journals_dbsk_LangeVDN11, author = {Lange, Dustin and Vogel, Tobias and Draisbach, Uwe and Naumann, Felix}, journal = {Datenbank-Spektrum}, keywords = {isg}, number = 1, pages = {51-57}, title = {Projektseminar "Similarity Search Algorithms"}, volume = 11, year = 2011 }
• Bleiholder, J., Herschel, M., Naumann, F.: Eliminating NULLs with Subsumption and Complementation. IEEE Data Engineering Bulletin. 34, 18-25 (2011).
  [ BibTeX ]
   
  @article{Jens2011a, author = {Bleiholder, Jens and Herschel, Melanie and Naumann, Felix}, journal = {IEEE Data Engineering Bulletin}, keywords = {isg}, number = 3, pages = {18-25}, title = {Eliminating NULLs with Subsumption and Complementation}, volume = 34, year = 2011 }
• Bleiholder, J., Naumann, F.: Kurz erklärt: Datenfusion. Datenbank-Spektrum. 11, 59-61 (2011).
  [ BibTeX ]
   
  @article{journals_dbsk_BleiholderN11, author = {Bleiholder, Jens and Naumann, Felix}, journal = {Datenbank-Spektrum}, keywords = {isg}, number = 1, pages = {59-61}, title = {Kurz erklärt: Datenfusion}, volume = 11, year = 2011 }

2010

• Bauckmann, J., Leser, U., Naumann, F.: Efficient and Exact Computation of Inclusion Dependencies for Data Integration. Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam (2010).
  [ BibTeX ] [ Download ]
   
  @techreport{techrep_spider_2010, author = {Bauckmann, Jana and Leser, Ulf and Naumann, Felix}, institution = {Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam}, keywords = {isg}, note = {ISBN 978-3-86956-048-9, ISSN 1613-5652}, number = 34, title = {Efficient and Exact Computation of Inclusion Dependencies for Data Integration}, year = 2010 }
• Lange, D., Böhm, C., Naumann, F.: Extracting structured information from Wikipedia articles to populate infoboxes. Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam (2010).
  [ BibTeX ] [ Download ]
   
  @techreport{techrep_iPopulator_2010, author = {Lange, Dustin and Böhm, Christoph and Naumann, Felix}, institution = {Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam}, keywords = {isg}, note = {ISBN 978-3-86956-081-6, ISSN 1613-5652}, number = 38, title = {Extracting structured information from Wikipedia articles to populate infoboxes}, year = 2010 }
• Dong, X., Naumann, F. eds: Proceedings of the 13th International Workshop on the Web and Databases (WebDB), Indianapolis, IN. (2010).
  [ BibTeX ]
   
  @proceedings{conf_webdb_2010, booktitle = {WebDB}, editor = {Dong, Xin and Naumann, Felix}, keywords = {isg}, title = {Proceedings of the 13th International Workshop on the Web and Databases (WebDB), Indianapolis, IN}, year = 2010 }
• Manolescu, I., Spaccapietra, S., Teubner, J., Kitsuregawa, M., Léger, A., Naumann, F., Ailamaki, A., Özcan, F. eds: Proceedings of the 13th International Conference on Extending Database Technology, Lausanne, Switzerland. ACM (2010).
  [ BibTeX ]
   
  @proceedings{conf_edbt_2010, booktitle = {EDBT}, editor = {Manolescu, Ioana and Spaccapietra, Stefano and Teubner, Jens and Kitsuregawa, Masaru and Léger, Alain and Naumann, Felix and Ailamaki, Anastasia and Özcan, Fatma}, keywords = {isg}, publisher = {ACM}, series = {ACM International Conference Proceeding Series}, title = {Proceedings of the 13th International Conference on Extending Database Technology, Lausanne, Switzerland}, volume = 426, year = 2010 }
• Bleiholder, J., Szott, S., Herschel, M., Kaufer, F., Naumann, F.: Subsumption and complementation as data fusion operators. Proceedings of the International Conference on Extending Database Technology (EDBT). pp. 513-524. , Lausanne, Switzerland (2010).
  [ BibTeX ]
   
  @inproceedings{conf_edbt_BleiholderSHKN10, address = {Lausanne, Switzerland}, author = {Bleiholder, Jens and Szott, Sascha and Herschel, Melanie and Kaufer, Frank and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Extending Database Technology (EDBT)}, keywords = {isg}, pages = {513-524}, title = {Subsumption and complementation as data fusion operators}, year = 2010 }
• Draisbach, U., Naumann, F.: DuDe: The Duplicate Detection Toolkit. Proceedings of the International Workshop on Quality in Databases (QDB). , Singapore (2010).
  [ BibTeX ] [ Download ]
   
  @inproceedings{DraisbachNaumannQDB2010, address = {Singapore}, author = {Draisbach, Uwe and Naumann, Felix}, booktitle = {Proceedings of the International Workshop on Quality in Databases (QDB)}, keywords = {isg}, title = {{DuDe}: The Duplicate Detection Toolkit}, year = 2010 }
• AbuJarour, M., Naumann, F., Craculeac, M.: Collecting, Annotating, and Classifying Public Web Services. On the Move to Meaningful Internet Systems: OTM - Confederated International Conferences: CoopIS, IS, DOA and ODBASE, Hersonissos, Crete, Greece, Proceedings, Part I. pp. 256-272 (2010).
  [ BibTeX ]
   
  @inproceedings{conf_otm_AbuJarourNC10, author = {AbuJarour, Mohammed and Naumann, Felix and Craculeac, Mircea}, booktitle = {On the Move to Meaningful Internet Systems: OTM - Confederated International Conferences: CoopIS, IS, DOA and ODBASE, Hersonissos, Crete, Greece, Proceedings, Part I}, keywords = {isg}, pages = {256-272}, title = {Collecting, Annotating, and Classifying Public Web Services}, year = 2010 }
• Böhm, C., Lorey, J., Fenz, D., Kny, E., Pohl, M., Naumann, F.: Creating voiD Descriptions for Web-Scale Data. Billion Triples Challenge (BTC) at the 9th International Semantic Web Conference (ISWC). , Shanghai, China (2010).
  [ BibTeX ] [ Download ]
   
  @inproceedings{Boehm10b, address = {Shanghai, China}, author = {Böhm, Christoph and Lorey, Johannes and Fenz, Dandy and Kny, Eyk and Pohl, Matthias and Naumann, Felix}, booktitle = {Billion Triples Challenge (BTC) at the 9th International Semantic Web Conference (ISWC)}, keywords = {isg}, note = {Winner}, title = {Creating voiD Descriptions for Web-Scale Data}, year = 2010 }
• AbuJarour, M., Naumann, F.: Dynamic tags for dynamic data web services. Proceedings of the 5th Workshop on Emerging Web Services Technology (WEWST), Ayia Napa, Cyprus, Greecc. pp. 3-9 (2010).
  [ BibTeX ]
   
  @inproceedings{conf_ecows_AbuJarourN10, author = {AbuJarour, Mohammed and Naumann, Felix}, booktitle = {Proceedings of the 5th Workshop on Emerging Web Services Technology (WEWST), Ayia Napa, Cyprus, Greecc}, keywords = {isg}, pages = {3-9}, title = {Dynamic tags for dynamic data web services}, year = 2010 }
• Böhm, C., Naumann, F., Freitag, M., George, S., Höfler, N., Köppelmann, M., Lehmann, C., Mascher, A., Schmidt, T.: Linking open government data: what journalists wish they had known. Proceedings the 6th International Conference on Semantic Systems (I-SEMANTICS), Graz, Austria (2010).
  [ BibTeX ] [ URL ]
   
  @inproceedings{conf_i-semantics_BohmNFGHKLMS10, author = {Böhm, Christoph and Naumann, Felix and Freitag, Markus and George, Stefan and Höfler, Norman and Köppelmann, Martin and Lehmann, Claudia and Mascher, Andrina and Schmidt, Tobias}, booktitle = {Proceedings the 6th International Conference on Semantic Systems (I-SEMANTICS), Graz, Austria}, keywords = {isg}, title = {Linking open government data: what journalists wish they had known}, year = 2010 }
• Lange, D., Böhm, C., Naumann, F.: Extracting structured information from Wikipedia articles to populate infoboxes. Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM). pp. 1661-1664. , Toronto, Canada (2010).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_cikm_LangeBN10, address = {Toronto, Canada}, author = {Lange, Dustin and Böhm, Christoph and Naumann, Felix}, booktitle = {Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {1661-1664}, title = {Extracting structured information from Wikipedia articles to populate infoboxes}, year = 2010 }
• AbuJarour, M., Naumann, F.: Towards a diamond SOA operational model. IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2010, 13-15 December 2010, Perth, Australia. pp. 1-4 (2010).
  [ BibTeX ]
   
  @inproceedings{conf_soca_AbuJarourN10, author = {AbuJarour, Mohammed and Naumann, Felix}, booktitle = {IEEE International Conference on Service-Oriented Computing and Applications, SOCA 2010, 13-15 December 2010, Perth, Australia}, keywords = {isg}, pages = {1-4}, title = {Towards a diamond SOA operational model}, year = 2010 }
• Bleiholder, J., Szott, S., Herschel, M., Naumann, F.: Complement union for data integration. Proceedings of the International Conference on Data Engineering Workshops, (ICDE NTII workshop). pp. 183-186. , Long Beach, CA (2010).
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  @inproceedings{conf_icde_BleiholderSHN10, address = {Long Beach, CA}, author = {Bleiholder, Jens and Szott, Sascha and Herschel, Melanie and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Data Engineering Workshops, (ICDE NTII workshop)}, keywords = {isg}, pages = {183-186}, title = {Complement union for data integration}, year = 2010 }
• Böhm, C., Naumann, F., Abedjan, Z., Fenz, D., Gruetze, T., Hefenbrock, D., Pohl, M., Sonnabend, D.: Profiling linked open data with ProLOD. Workshops Proceedings of the 26th International Conference on Data Engineering (ICDE), Long Beach, CA. pp. 175-178 (2010).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_icde_BohmNAFGHPS10, author = {Böhm, Christoph and Naumann, Felix and Abedjan, Ziawasch and Fenz, Dandy and Gruetze, Toni and Hefenbrock, Daniel and Pohl, Matthias and Sonnabend, David}, booktitle = {Workshops Proceedings of the 26th International Conference on Data Engineering (ICDE), Long Beach, CA}, keywords = {isg}, pages = {175-178}, title = {Profiling linked open data with ProLOD}, year = 2010 }
• Vogel, T.: Self-Adaptive Data Quality Web Services. Grundlagen von Datenbanken. , Bad Helmstedt (2010).
  [ BibTeX ] [ Download ]
   
  @inproceedings{dqws, address = {Bad Helmstedt}, author = {Vogel, Tobias}, booktitle = {Grundlagen von Datenbanken}, keywords = {isg}, title = {Self-Adaptive Data Quality Web Services}, year = 2010 }
• Lorey, J., Naumann, F.: Towards Granular Data Placement Strategies for Cloud Platforms. Proceedings of the 6th International Conference on Granular Computing (GrC). pp. 346-351. , San Jose, California, USA (2010).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_grc_LoreyN10, address = {San Jose, California, USA}, author = {Lorey, Johannes and Naumann, Felix}, booktitle = {Proceedings of the 6th International Conference on Granular Computing (GrC)}, keywords = {isg}, pages = {346-351}, title = {Towards Granular Data Placement Strategies for Cloud Platforms}, year = 2010 }
• Brauer, F., Huber, M., Hackenbroich, G., Leser, U., Naumann, F., Barczynski, W.M.: Graph-based concept identification and disambiguation for enterprise search. Proceedings of the 19th International Conference on World Wide Web (WWW). pp. 171-180. , Raleigh, NC (2010).
  [ BibTeX ]
   
  @inproceedings{conf_www_BrauerHHLNB10, address = {Raleigh, NC}, author = {Brauer, Falk and Huber, Michael and Hackenbroich, Gregor and Leser, Ulf and Naumann, Felix and Barczynski, Wojciech M.}, booktitle = {Proceedings of the 19th International Conference on World Wide Web (WWW)}, keywords = {isg}, pages = {171-180}, title = {Graph-based concept identification and disambiguation for enterprise search}, year = 2010 }
• Naumann, F., Herschel, M.: An Introduction to Duplicate Detection. Morgan & Claypool Publishers (2010).
  [ BibTeX ]
   
  @book{series_synthesis_2010Naumann, author = {Naumann, Felix and Herschel, Melanie}, booktitle = {An Introduction to Duplicate Detection}, keywords = {isg}, publisher = {Morgan {&} Claypool Publishers}, series = {Synthesis Lectures on Data Management}, title = {An Introduction to Duplicate Detection}, year = 2010 }
• Dong, X.L., Naumann, F.: 13th international workshop on the web and databases: WebDB 2010. SIGMOD Record. 39, 37-39 (2010).
  [ BibTeX ]
   
  @article{journals_sigmod_DongN10, author = {Dong, Xin Luna and Naumann, Felix}, journal = {SIGMOD Record}, keywords = {isg}, number = 3, pages = {37-39}, title = {13th international workshop on the web and databases: WebDB 2010}, volume = 39, year = 2010 }

2009

• Draisbach, U., Naumann, F.: A Comparison and Generalization of Blocking and Windowing Algorithms for Duplicate Detection. Proceedings of the International Workshop on Quality in Databases (QDB). , Lyon, France (2009).
  [ BibTeX ] [ Download ]
   
  @inproceedings{DraisbachNaumannQDB2009, address = {Lyon, France}, author = {Draisbach, Uwe and Naumann, Felix}, booktitle = {Proceedings of the International Workshop on Quality in Databases (QDB)}, keywords = {isg}, title = {A Comparison and Generalization of Blocking and Windowing Algorithms for Duplicate Detection}, year = 2009 }
• AbuJarour, M., Craculeac, M., Menge, F., Vogel, T., Schwarz, J.-F.: POSR: A Comprehensive System for Aggregating and Using Web Services (demo). Proceedings of the IEEE Services Cup 2009 at IEEE International Conference on Web Services (ICWS) (2009).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  Recently, the number of public Web Services has been constantly increasing. Nevertheless, consuming Web Services as an end-user is not straightforward, because creating a suitable user interface for consuming a Web Service requires much effort. In this work, we introduce a novel approach where user interface fragments for consuming Web Services are generated automatically, and aggregated and customized by end-users to match their preferences. Users can collaboratively improve the auto-generated user interfaces and share them among each other. Our three main sources of Web Services are explicit registration, automatic identification and collecting over the Web, as well as extraction and generation from existing web applications. We validated our approach by implementing it as a comprehensive system coined “Posr”.
  @inproceedings{posr, abstract = {Recently, the number of public Web Services has been constantly increasing. Nevertheless, consuming Web Services as an end-user is not straightforward, because creating a suitable user interface for consuming a Web Service requires much effort. In this work, we introduce a novel approach where user interface fragments for consuming Web Services are generated automatically, and aggregated and customized by end-users to match their preferences. Users can collaboratively improve the auto-generated user interfaces and share them among each other. Our three main sources of Web Services are explicit registration, automatic identification and collecting over the Web, as well as extraction and generation from existing web applications. We validated our approach by implementing it as a comprehensive system coined “Posr”.}, author = {AbuJarour, Mohammed and Craculeac, Mircea and Menge, Falko and Vogel, Tobias and Schwarz, Jan-Felix}, booktitle = {Proceedings of the IEEE Services Cup 2009 at IEEE International Conference on Web Services (ICWS)}, keywords = {isg}, title = {POSR: A Comprehensive System for Aggregating and Using Web Services (demo)}, year = 2009 }
• Rostin, A., Albrecht, O., Bauckmann, J., Naumann, F., Leser, U.: A Machine Learning Approach to Foreign Key Discovery. Proceedings of the International Workshop on the Web and Databases (WebDB). , Providence, RI (2009).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_webdb_RostinABNL09, address = {Providence, RI}, author = {Rostin, Alexandra and Albrecht, Oliver and Bauckmann, Jana and Naumann, Felix and Leser, Ulf}, booktitle = {Proceedings of the International Workshop on the Web and Databases (WebDB)}, keywords = {isg}, title = {A Machine Learning Approach to Foreign Key Discovery}, year = 2009 }
• Böhm, C., Groth, P., Leser, U.: Graph-Based Ontology Construction from Heterogenous Evidences. Proceedings of the International Semantic Web Conference (ISWC). pp. 91-96 (2009).
  [ BibTeX ]
   
  @inproceedings{iswc2009_boehm, author = {Böhm, Christoph and Groth, Philip and Leser, Ulf}, booktitle = {Proceedings of the International Semantic Web Conference (ISWC)}, keywords = {isg}, pages = {91-96}, title = {Graph-Based Ontology Construction from Heterogenous Evidences}, year = 2009 }
• Albrecht, A., Naumann, F.: METL: Managing and Integrating ETL Processes. Proceedings of the VLDB PhD Workshop. Co-located with the 35th International Conference on Very Large Data Bases (VLDB), Lyon, France (2009).
  [ BibTeX ]
   
  @inproceedings{conf_vldb_AlbrechtN09, author = {Albrecht, Alexander and Naumann, Felix}, booktitle = {Proceedings of the VLDB PhD Workshop. Co-located with the 35th International Conference on Very Large Data Bases (VLDB), Lyon, France}, keywords = {isg}, title = {METL: Managing and Integrating ETL Processes}, year = 2009 }
• Vogel, T., Kaufer, F., Naumann, F.: Encapsulating Multi-stepped Web Forms as Web Services. Proceedings of the 7th International Conference on Service-Oriented Computing (ICSOC). pp. 488-497 (2009).
  [ Abstract ] [ BibTeX ] [ Download ]
   
  HTML forms are the predominant interface between users and web applications. Many of these applications display a sequence of multiple forms on separate pages, for instance to book a flight or order a DVD. We introduce a method to wrap these multi-stepped forms and offer their individual functionality as a single consolidated Web Service. This Web Service in turn maps input data to the individual forms in the correct order. Such consolidation better enables operation of the forms by applications and provides a simpler interface for human users. To this end we analyze the HTML code and sample user interaction of each page and infer the internal model of the application. A particular challenge is to map semantically same fields across multiple forms and choose meaningful labels for them. Web Service output is parsed from the resulting HTML page. Experiments on different multi-stepped web forms show the feasibility and usefulness of our approach.
  @inproceedings{conf_icsoc_VogelKN09, abstract = {HTML forms are the predominant interface between users and web applications. Many of these applications display a sequence of multiple forms on separate pages, for instance to book a flight or order a DVD. We introduce a method to wrap these multi-stepped forms and offer their individual functionality as a single consolidated Web Service. This Web Service in turn maps input data to the individual forms in the correct order. Such consolidation better enables operation of the forms by applications and provides a simpler interface for human users. To this end we analyze the HTML code and sample user interaction of each page and infer the internal model of the application. A particular challenge is to map semantically same fields across multiple forms and choose meaningful labels for them. Web Service output is parsed from the resulting HTML page. Experiments on different multi-stepped web forms show the feasibility and usefulness of our approach.}, author = {Vogel, Tobias and Kaufer, Frank and Naumann, Felix}, booktitle = {Proceedings of the 7th International Conference on Service-Oriented Computing (ICSOC)}, keywords = {isg}, pages = {488-497}, title = {Encapsulating Multi-stepped Web Forms as Web Services}, year = 2009 }
• Naumann, F., Raschid, L.: Guest Editorial for the Special Issue on Data Quality in Databases. Journal on Data and Information Quality (JDIQ). 1, (2009).
  [ BibTeX ]
   
  @article{journals_jdiq_NaumannR09, author = {Naumann, Felix and Raschid, Louiqa}, journal = {Journal on Data and Information Quality (JDIQ)}, keywords = {isg}, number = 2, title = {Guest Editorial for the Special Issue on Data Quality in Databases}, volume = 1, year = 2009 }
• Dong, X.L., Naumann, F.: Data fusion - Resolving Data Conflicts for Integration (tutorial). Proceedings of the VLDB. 2, 1654-1655 (2009).
  [ BibTeX ]
   
  @article{journals_pvldb_DongN09, author = {Dong, Xin Luna and Naumann, Felix}, journal = {Proceedings of the VLDB}, keywords = {isg}, number = 2, pages = {1654-1655}, title = {Data fusion - Resolving Data Conflicts for Integration (tutorial)}, volume = 2, year = 2009 }

2008

• Herschel, M., Naumann, F.: Scaling up duplicate detection in graph data. Proceedings of the ACM Conference on Information and Knowledge Management (CIKM). pp. 1325-1326. , Napa Valley, CA (2008).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_cikm_HerschelN08, address = {Napa Valley, CA}, author = {Herschel, Melanie and Naumann, Felix}, booktitle = {Proceedings of the ACM Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {1325-1326}, title = {Scaling up duplicate detection in graph data}, year = 2008 }
• Albrecht, A., Naumann, F.: Managing ETL Processes. Proceedings of the International Workshop on New Trends in Information Integration, (NTII), Auckland, New Zealand. pp. 12-15 (2008).
  [ BibTeX ]
   
  @inproceedings{conf_ntii_AlbrechtN08, author = {Albrecht, Alexander and Naumann, Felix}, booktitle = {Proceedings of the International Workshop on New Trends in Information Integration, (NTII), Auckland, New Zealand}, keywords = {isg}, pages = {12-15}, title = {Managing ETL Processes}, year = 2008 }
• Jacob, M., Kuscher, A., Thiele, C., Plauth, M.: Automated data augmentation services using text mining, data cleansing and web crawling techniques. IEEE Congress on Services (2008).
  [ BibTeX ]
   
  @inproceedings{Jacob08, author = {Jacob, Matthias and Kuscher, Alexander and Thiele, Christoph and Plauth, Max}, booktitle = {IEEE Congress on Services}, keywords = {isg}, title = {Automated data augmentation services using text mining, data cleansing and web crawling techniques}, year = 2008 }
• Bleiholder, J., Naumann, F.: Data fusion. ACM Computing Surveys. 41, (2008).
  [ BibTeX ]
   
  @article{journals_csur_BleiholderN08, author = {Bleiholder, Jens and Naumann, Felix}, journal = {ACM Computing Surveys}, keywords = {isg}, number = 1, title = {Data fusion}, volume = 41, year = 2008 }
• Hose, K., Roth, A., Zeitz, A., Sattler, K.-U., Naumann, F.: A research agenda for query processing in large-scale peer data management systems. Information Systems (IS). 33, 597-610 (2008).
  [ BibTeX ]
   
  @article{journals_is_HoseRZSN08, author = {Hose, Katja and Roth, Armin and Zeitz, Andre and Sattler, Kai-Uwe and Naumann, Felix}, journal = {Information Systems (IS)}, keywords = {isg}, number = {7-8}, pages = {597-610}, title = {A research agenda for query processing in large-scale peer data management systems}, volume = 33, year = 2008 }
• Weis, M., Naumann, F., Jehle, U., Lufter, J., Schuster, H.: Industry-scale duplicate detection. Proceedings of the VLDB. 1, 1253-1264 (2008).
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  @article{journals_pvldb_WeisNJLS08, author = {Weis, Melanie and Naumann, Felix and Jehle, Ulrich and Lufter, Jens and Schuster, Holger}, journal = {Proceedings of the VLDB}, keywords = {isg}, number = 2, pages = {1253-1264}, title = {Industry-scale duplicate detection}, volume = 1, year = 2008 }

2007

• Ganti, V., Naumann, F. eds: Proceedings of the Fifth International Workshop on Quality in Databases (QDB). , Vienna, Austria (2007).
  [ BibTeX ]
   
  @proceedings{conf_iqis_2007, address = {Vienna, Austria}, booktitle = {QDB}, editor = {Ganti, Venkatesh and Naumann, Felix}, keywords = {isg}, title = {Proceedings of the Fifth International Workshop on Quality in Databases (QDB)}, year = 2007 }
• Albrecht, A., Naumann, F.: Networked PIM using PDMS. Proceedings of the International Workshop Networking Meets Databases (NetDB) (2007).
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  @inproceedings{Albrecht07, author = {Albrecht, Alexander and Naumann, Felix}, booktitle = {Proceedings of the International Workshop Networking Meets Databases (NetDB)}, keywords = {isg}, title = {Networked {PIM} using {PDMS}}, year = 2007 }
• Naumann, F.: Schema- und Metadatenmanagement in Peer Data Management Systemen. Datenbanksysteme in Business, Technologie und Web (BTW), Workshop Proceedings. p. 3. , Aachen, Germany (2007).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_btw_Naumann07, address = {Aachen, Germany}, author = {Naumann, Felix}, booktitle = {Datenbanksysteme in Business, Technologie und Web (BTW), Workshop Proceedings}, keywords = {isg}, pages = 3, title = {Schema- und Metadatenmanagement in Peer Data Management Systemen}, year = 2007 }
• Hipp, J., Müller, M., Hohendorff, J., Naumann, F.: Rule-Based Measurement Of Data Quality In Nominal Data. Proceedings of the International Conference on Information Quality (ICIQ). pp. 364-378. , Cambridge, MA (2007).
  [ BibTeX ] [ Download ]
   
  @inproceedings{conf_iq_HippMHN07, address = {Cambridge, MA}, author = {Hipp, Jochen and Müller, Markus and Hohendorff, Johannes and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information Quality (ICIQ)}, keywords = {isg}, pages = {364-378}, title = {Rule-Based Measurement Of Data Quality In Nominal Data}, year = 2007 }
• Roth, A., Naumann, F.: System P: Completeness-driven Query Answering in Peer Data Management Systems (demo). Datenbanksysteme in Business, Technologie und Web (BTW), Aachen, Germany. pp. 625-628 (2007).
  [ BibTeX ]
   
  @inproceedings{conf_btw_RothN07, author = {Roth, Armin and Naumann, Felix}, booktitle = {Datenbanksysteme in Business, Technologie und Web (BTW), Aachen, Germany}, keywords = {isg}, pages = {625-628}, title = {System P: Completeness-driven Query Answering in Peer Data Management Systems (demo)}, year = 2007 }
• Bauckmann, J., Leser, U., Naumann, F., Tietz, V.: Efficiently Detecting Inclusion Dependencies. Proceedings of the International Conference on Data Engineering (ICDE). pp. 1448-1450. , Istanbul, Turkey (2007).
  [ BibTeX ]
   
  @inproceedings{conf_icde_BauckmannLNT07, address = {Istanbul, Turkey}, author = {Bauckmann, Jana and Leser, Ulf and Naumann, Felix and Tietz, Veronique}, booktitle = {Proceedings of the International Conference on Data Engineering (ICDE)}, keywords = {isg}, pages = {1448-1450}, title = {Efficiently Detecting Inclusion Dependencies}, year = 2007 }
• Raschid, L., Vidal, M.E., Wu, Y., Naumann, F., Bleiholder, J.: Answering Top K Queries Efficiently with Overlap of Answers in Sources or Source Paths. Proceedings of the International Workshop on Information Integration on the Web (IIWeb) (2007).
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  @inproceedings{Raschid07, author = {Raschid, Louiqa and Vidal, Maria Esther and Wu, Yao and Naumann, Felix and Bleiholder, Jens}, booktitle = {Proceedings of the International Workshop on Information Integration on the Web (IIWeb)}, keywords = {isg}, title = {Answering Top K Queries Efficiently with Overlap of Answers in Sources or Source Paths}, year = 2007 }
• Führing, P., Naumann, F.: Emergent Data Quality Annotation And Visualization. Proceedings of the International Conference on Information Quality (ICIQ). pp. 424-430. , Cambridge, MA (2007).
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  @inproceedings{conf_iq_FuhringN07, address = {Cambridge, MA}, author = {Führing, Paul and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information Quality (ICIQ)}, keywords = {isg}, pages = {424-430}, title = {Emergent Data Quality Annotation And Visualization}, year = 2007 }
• Legler, F., Naumann, F.: A Classification of Schema Mappings and Analysis of Mapping Tools. Proceedings of Datenbanksysteme in Business, Technologie und Web (BTW). pp. 449-464. , Aachen, Germany (2007).
  [ BibTeX ]