Alhosseini, S.A., Bin Tareaf, R., Najafi, P., Meinel, C.: Detect Me If You Can: Spam Bot Detection Using Inductive Representation Learning.WWW19, World Wide Web Conference. ACM, San Francisco, USA (2019).
Bin Tareaf, R., Alhosseini, S.A., Meinel, C.: Cross-Platform Personality Exploration System for Online Social Networks: Facebook vs. Twitter.Journal of Web Intelligence Consortium (WIC). (2019).
Bin Tareaf, R., Alhosseini, S.A., Berger, P., Hennig, P., Meinel, C.: Towards Automatic Personality Prediction Using Facebook Likes Metadata. In: 14th International Conference on Intelligent Systems, I.E.E.E. and Knowledge Engineering, D. (eds.) The 14th IEEE International Conference on Intelligent Systems and Knowledge Engineering. IEEE, Dalian, China (2019).
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Editor(s)14th International Conference on Intelligent Systems, IEEE and Knowledge Engineering, Dalian, China.
Bin Tareaf, R., Berger, P., Hennig, P., Meinel, C.: Personality Exploration System for Online Social Networks: Facebook Brands As a Use Case.IEEE/WIC/ACM International Conference on Web Intelligence. IEEE Press, Santiago, Chile (2019).
User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands’ pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
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AbstractUser-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands’ pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
Bin Tareaf, R., Alhosseini, S.A., Meinel, C.: Facial-Based Personality Prediction Models For Estimating Individuals Private Traits.The 12th IEEE International Conference on Social Computing and Networking ( IEEE SocialCom)). IEEE, Xiamen, China (2019).
Traifeh, H., Bin Tareaf, R., Meinel, C.: Challenges and Opportunities in Digital Learning: Perspectives from the Arab World.British Association for International & Comparative Education, BAICE 2018. BAICE UK, University of York, UK (2018).
The plethora of challenges facing the education sector in the Arab World is resulting in significant gaps in outcome and quality between the region and countries with similar levels of economic development. However, the past few years witnessed a gradual trend of improvement propelled by an increasing adoption of digital learning. The widespread of the Internet, the increased number of mobile devices (especially smartphones) among Arab youth and the improvement of some educational institutions’ technical infrastructure, led to a thriving online education landscape. This paper provides a systematic review of Arab digital learning and MOOCs platforms, and highlights the current challenges and issues faced by learners and providers. The paper reports the results of a survey of Arab students and life-long learners about their digital learning experiences. Analysis of the survey data shows that despite its growing adoption, digital learning is still in its early stages compared to that of the developed countries. The paper discusses the identified opportunities for improvement and the potential benefits and advantages for both learners and digital learning providers. The paper concludes with a discussion of future research directions.
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AbstractThe plethora of challenges facing the education sector in the Arab World is resulting in significant gaps in outcome and quality between the region and countries with similar levels of economic development. However, the past few years witnessed a gradual trend of improvement propelled by an increasing adoption of digital learning. The widespread of the Internet, the increased number of mobile devices (especially smartphones) among Arab youth and the improvement of some educational institutions’ technical infrastructure, led to a thriving online education landscape. This paper provides a systematic review of Arab digital learning and MOOCs platforms, and highlights the current challenges and issues faced by learners and providers. The paper reports the results of a survey of Arab students and life-long learners about their digital learning experiences. Analysis of the survey data shows that despite its growing adoption, digital learning is still in its early stages compared to that of the developed countries. The paper discusses the identified opportunities for improvement and the potential benefits and advantages for both learners and digital learning providers. The paper concludes with a discussion of future research directions.
Bin Tareaf, R., Berger, P., Hennig, P., Meinel, C.: Malicious Behaviour Identification in Online Social Networks.Springer - IFIP International Conference on Distributed Applications and Interoperable Systems. p. 18. Springer LNCS, Madrid, Spain (2018).
This paper outlines work on the detection of anomalous behaviour in Online Social Networks (OSNs). We present various automated techniques for identifying a ‘prodigious’ segment within a tweet, and consider tweets which are unusual because of writing style, posting sequence, or engagement level. We evaluate the mechanism by running extensive experiments over large artificially constructed tweets corpus, crawled to include randomly interpolated and abnormal Tweets. In order to successfully identify anomalies in a tweet, we aggregate more than 21 features to characterize users’ behavioural pattern. Using these features with each of our methods, we examine the effect of the total number of tweets on our ability to detect an anomaly, allowing segments of size 50 tweets 100 tweets and 200 tweets. We show indispensable improvements over a baseline in all circumstances for each method, and identify the method variant which performs persistently better than others.
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AbstractThis paper outlines work on the detection of anomalous behaviour in Online Social Networks (OSNs). We present various automated techniques for identifying a ‘prodigious’ segment within a tweet, and consider tweets which are unusual because of writing style, posting sequence, or engagement level. We evaluate the mechanism by running extensive experiments over large artificially constructed tweets corpus, crawled to include randomly interpolated and abnormal Tweets. In order to successfully identify anomalies in a tweet, we aggregate more than 21 features to characterize users’ behavioural pattern. Using these features with each of our methods, we examine the effect of the total number of tweets on our ability to detect an anomaly, allowing segments of size 50 tweets 100 tweets and 200 tweets. We show indispensable improvements over a baseline in all circumstances for each method, and identify the method variant which performs persistently better than others.
Bin Tareaf, R., Berger, P., Hennig, P., Meinel, C.: ASEDS: Towards Automatic Social Emotion Detection System Using Facebook Reactions.2018 IEEE 20th International Conference on High Performance Computing and Communications. p. 860--866. IEEE Press, Exeter, UK (2018).
The Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for “Joy” emotion.
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AbstractThe Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for “Joy” emotion.
Bin Tareaf, R., Berger, P., Hennig, P., Jung, J., Meinel, C.: Identifying Audience Attributes - Predicting Age, Gender and Personality for Enhanced Article Writing.ACM - International Conference on Cloud and Big Data Computing. pp. 79-88. ACM Press, London, UK (2017).
In order to create an effective article, having great content is essential. However, to achieve this, the writer needs to target a specific audience. A target audience refers to a group of readers that a writer intends to reach with his content. Defining a target audience is substantial because it has a direct effect on adjusting writing style and content of the article. Nowadays, writers rely solely on annotated attributes of articles, such as location and language to understand his/her audience. The aim of this work is to identify the audience attributes of articles, especially not-annotated attributes. Among others, this work focuses on the detection of three key audience attributes of related articles: age, gender, and personality. We compare between multiple machine learning classifiers to detect these attributes. Finally, we demonstrate a prototypical application that enables writers to run existing algorithms such as trend detection and showing related articles that are specific to a defined target audience based on the newly detected attributes.
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AbstractIn order to create an effective article, having great content is essential. However, to achieve this, the writer needs to target a specific audience. A target audience refers to a group of readers that a writer intends to reach with his content. Defining a target audience is substantial because it has a direct effect on adjusting writing style and content of the article. Nowadays, writers rely solely on annotated attributes of articles, such as location and language to understand his/her audience. The aim of this work is to identify the audience attributes of articles, especially not-annotated attributes. Among others, this work focuses on the detection of three key audience attributes of related articles: age, gender, and personality. We compare between multiple machine learning classifiers to detect these attributes. Finally, we demonstrate a prototypical application that enables writers to run existing algorithms such as trend detection and showing related articles that are specific to a defined target audience based on the newly detected attributes.
Bin Tareaf, R., Berger, P., Hennig, P., Koall, S., Kohstall, J., Meinel, C.: Information Propagation Speed and Patterns in Social Networks: a Case Study Analysis of German Tweets.JCP 2018. pp. 761-770. ACM & Journal of Computer (ISSN: 1796-203X), Jeju Island, South Korea (2017).
In this paper, we present our experiences in analyzing Twitter data. The analysis has shown that information diffuses over time through the Twitter network in certain patterns. Furthermore, it has shown those friend relationships significantly influence the information propagation speed on Twitter. Since it was launched in 2006, the microblogging service grew tremendously. Tweets are sent by users all around the world. Results show that there are two major patterns. While these patterns accommodate us to understand the diffusion of information through Twitter in an even better plan, the analysis of friend networks provides information on who influences the network, concerning the number of re-tweets and the time between a tweet and its re-tweets. The approaches have been evaluated both technically, based on how certain a topic matches one of the patterns and how prominent friends are compared to other users, and conceptually, based on existing, well-known approaches in measuring the speed and scale of information diffusion on Twitter.
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AbstractIn this paper, we present our experiences in analyzing Twitter data. The analysis has shown that information diffuses over time through the Twitter network in certain patterns. Furthermore, it has shown those friend relationships significantly influence the information propagation speed on Twitter. Since it was launched in 2006, the microblogging service grew tremendously. Tweets are sent by users all around the world. Results show that there are two major patterns. While these patterns accommodate us to understand the diffusion of information through Twitter in an even better plan, the analysis of friend networks provides information on who influences the network, concerning the number of re-tweets and the time between a tweet and its re-tweets. The approaches have been evaluated both technically, based on how certain a topic matches one of the patterns and how prominent friends are compared to other users, and conceptually, based on existing, well-known approaches in measuring the speed and scale of information diffusion on Twitter.
Berger, P., Hennig, P., Dummer, D., Ernst, A., Hille, T., Schulze, F., Meinel, C.: Extracting Image Context from Pinterest for Image Recommendation.Proceedings of the 8th IEEE International Conference on Social Computing and Networking (SocialCom2015). , Chengdu, China (2015).
Hennig, P., Berger, P., Brehm, M., Grasnick, B., Herdt, J., Meinel, C.: Hot spot detection - An interactive cluster heat map for sentiment analysis.Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015). , Paris, France (2015).
Berger, P., Hennig, P., Bunk, S., Korsch, D., Kurzynski, D., Meinel, C.: Finding Demand for Products in the Social Web.Proceedings of the 8th IEEE International Conference on Social Computing and Networking (SocialCom2015). , Chengdu, China (2015).
Berger, P., Hennig, P., Schönberg, M., Meinel, C.: Blog, Forum or Newspaper? Web Genre Detection using SVMs.Proceedings of the IEEE/ACM International Conference on Web Intelligence (WI2015). IEEE Press, Singapore (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).
Berger, P., Hennig, P., Eschrig, J., Roeder, D., Meinel, C.: Extraction and Analysis of Web Interviews.Proceedings of the IEEE/ACM International Conference on Web Intelligence (WI2015). IEEE Press, Singapore (2015).
Hennig, P., Berger, P., Kurzynski, D., Rantzsch, H., Meinel, C.: Efficient Event Detection for the Blogosphere.The 7th IEEE International Conference on Social Computing and Networking (SocialCom 2014) (2014).
Hennig, P., Berger, P., Lehmanny, C., Maschery, A., Meinel, C.: Accelerate the detection of Trends by using Sentiment Analysis within the Blogosphere.2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). pp. 503-508 (2014).
Hennig, P., Berger, P., Pirl, L., Schulze, L., Meinel, C.: Exploring Emotions over time within the Blogosphere.The 2014 IEEE International Conference on Data Science and Advanced Analytics (DSAA´14) (2014).
Hennig, P., Berger, P., Steuer, C., Wuerz, C., Meinel, C.: Cluster Labeling for the Blogosphere.The 7th IEEE International Conference on Social Computing and Networking (SocialCom 2014) (2014).
Berger, P., Hennig, P., Glaesery, D., Klementy, H., Meinel, C.: Geographic Focus Detection using Multiple Location Taggers.2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014).
Chujfi, S., Meinel, C.: Modeling cognitive style patterns to explore individuals’ capabilities for processing knowledge in virtual settings.Proceedings of the 2014 European Conference on Cognitive Ergonomics (ECCE 2014). ACM (2014).
Organizations continue building virtual working teams (Teleworkers) to become more dynamic as part of their strategic innovation with great benefits to individuals, business, and society. Geographically distributed organizations however have the big challenge of managing people’s knowledge not only to keep operations running but also to promote innovation within the organization creating new knowledge. This study analyses how knowledge-based organizations working with decentralized staff may need considering cognitive styles (CS) and learning styles (LS) of individuals participating on their programs to effectively manage knowledge in virtual settings. The study aims at modeling patterns to identify abilities of individuals according to their cognitive and learning styles attempting to match affinities to work remotely and take part in virtual team work, and also to correctly determine the use of appropriate hypermedia tools to help overcoming lower performance and effectiveness, which may occur due to the lack face-to-face communication normally found in typical offices.
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AbstractOrganizations continue building virtual working teams (Teleworkers) to become more dynamic as part of their strategic innovation with great benefits to individuals, business, and society. Geographically distributed organizations however have the big challenge of managing people’s knowledge not only to keep operations running but also to promote innovation within the organization creating new knowledge. This study analyses how knowledge-based organizations working with decentralized staff may need considering cognitive styles (CS) and learning styles (LS) of individuals participating on their programs to effectively manage knowledge in virtual settings. The study aims at modeling patterns to identify abilities of individuals according to their cognitive and learning styles attempting to match affinities to work remotely and take part in virtual team work, and also to correctly determine the use of appropriate hypermedia tools to help overcoming lower performance and effectiveness, which may occur due to the lack face-to-face communication normally found in typical offices.
Berger, P., Hennig, P., Detje, S., Eickhoff, D., Taschik, D., Wagner, B., Meinel, C.: A Topical Map of the Blogosphere.The Sixth ASE International Conference on Social Computing (2014).
Berger, P., Hennig, P., Petrick, D., Pursche, M., Meinel, C.: Adaptive Post Recognition.2014 ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014).
Hennig, P., Berger, P., Meinel, C.: Web Mining Accelerated with In-Memory and Column Store Technology.Proceedings of the 9th International Conference on Advanced Data Mining and Applications (ADMA 2013). pp. 205-216. Springer-Verlag Berlin Heidelberg 2013 (2013).
Hennig, P., Berger, P., Meinel, C.: Identify Emergent Trends based on the Blogosphere.Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI/IAT 2013). IEEE CS (2013).
Berger, P., Hennig, P., Meinel, C.: Identifying Domain Experts in the Blogosphere - Ranking Blogs based on Topic Consistency.Proceedings of IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI/IAT 2013). pp. 252-259. IEEE CS (2013).
Berger, P., Hennig, P., Klingbeil, T., Kohnen, M., Pade, S., Meinel, C.: Mining the Boundaries of Social Networks: Crawling Facebook and Twitter for BlogIntelligence.Proceedings of the International Conference on Information and Knowledge Engineering IKE'13. pp. 223-229 (2013).
Hennig, P., Berger, P., Meinel, C., Graber, M., Hildebrandt, J., Lehmann, S., Ramson, C.: Tracking Visitor Engagement in the Blogosphere for Leveraging Rankings.Proceedings of ASE/IEEE International Conference on Social Computing (SocialCom2013). pp. 365-372 (2013).
Grünewald, F., Mazandarani, E., Meinel, C., Teusner, R., Totschnig, M., Willems, C.: openHPI - a Case-Study on the Emergence of two Learning Communities.Proceedings of 2013 IEEE Global Engineering Education Conference (EDUCON). IEEE Press (2013).
Recently a new format of online education has emerged that combines video lectures, interactive quizzes and social learning into an event that aspires to attract a massive number of participants. This format, referred to as Massive Open Online Course (MOOC), has garnered considerable public attention, and has been invested with great hopes (and fears) of transforming higher education by opening up the walls of closed institutions to a world-wide audience. In this paper, we present two MOOCs that were hosted at the same platform, and have implemented the same learning design. Due to their difference in language, topic domain and difficulty, the communities that they brought into existence were very different. We start by describing the MOOC format in more detail, and the distinguishing features of openHPI. We then discuss the literature on communities of practice and cultures of participation. After some statistical data about the first openHPI course, we present our qualitative observations about both courses, and conclude by giving an outlook on an ongoing comparative analysis of the two courses.
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AbstractRecently a new format of online education has emerged that combines video lectures, interactive quizzes and social learning into an event that aspires to attract a massive number of participants. This format, referred to as Massive Open Online Course (MOOC), has garnered considerable public attention, and has been invested with great hopes (and fears) of transforming higher education by opening up the walls of closed institutions to a world-wide audience. In this paper, we present two MOOCs that were hosted at the same platform, and have implemented the same learning design. Due to their difference in language, topic domain and difficulty, the communities that they brought into existence were very different. We start by describing the MOOC format in more detail, and the distinguishing features of openHPI. We then discuss the literature on communities of practice and cultures of participation. After some statistical data about the first openHPI course, we present our qualitative observations about both courses, and conclude by giving an outlook on an ongoing comparative analysis of the two courses.
Hennig, P., Berger, P., Godde, C., Hoffmann, D., Meinel, C.: A Fuzzy, Incremental, Hierachical Approach of Clustering Huge Collections of Web Documents.Proceedings of the International Conference on Internet Computing and Big Data ICOMP'13. pp. 167-172 (2013).
Yang, H., Siebert, M., Lühne, P., Sack, H., Meinel, C.: Lecture Video Indexing and Analysis Using Video OCR Technology.Proceedings of the 7th International Conference on Signal Image Technology and Internet Based Systems (SITIS 2011). IEEE Press, Dijon, France (2011).
Hennig, P., Berger, P., Bross, J., Meinel, C.: Mapping the Blogosphere — Towards a Universal and Scalable Blog-Crawler.Proceedings of the Third IEEE International Conference on Social Computing (Social Com2011). pp. 672-677. IEEE CS, MIT, Boston, USA (2011).
Bross, J., Schilf, P., Jenders, M., Meinel, C.: Visualizing the Blogosphere with BlogConnect.Proceedings of the Third IEEE International Conference on Social Computing (SocialCom 2011). pp. 651 - 656. IEEE CS, MIT, Boston, USA (2011).
Yang, H., Siebert, M., Lühne, P., Sack, H., Meinel, C.: Automatic Lecture Video Indexing Using Video OCR Technology.Proceedings of the IEEE International Symposium on Multimedia 2011 (ISM 2011). IEEE Press, Dana Point, CA, USA (2011).
Yang, H., Oehlke, C., Meinel, C.: A Solution for German Speech Recognition for Analysis and Processing of Lecture Videos.Proceedings of the 10th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2011). IEEE Computer Society, Sanya, China (2011).
Bross, J., Noweski, C., Meinel, C.: Reviving the Innovative Process of Design Thinking.Proceedings of the 6th International Conference on Internet and Web Applications and Services (ICIW 2011) (2011).
Quasthoff, M., Völkel, M., Meinel, C.: Unsupervised Matching of Object Models and Ontologies.Proceedings of the 7th International Conference on Semantic Systems (I-Semantics 2010). p. 7. ACM Press, Graz, Austria (2010).
Bross, J., Quasthoff, M., Berger, P., Hennig, P., Meinel, C.: Mapping the blogosphere with RSS-feeds.Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA 2010). pp. 453-460. IEEE Press, Perth, Australia (2010).
Bross, J., Schilf, P., Meinel, C.: Visualizing Blog Archives to Explore Content- and Context-Related Interdependencies.Proceedings of the 9th IEEE / WIC / ACM International Conferences on Web Intelligence (WI 2010). pp. 647-652. ACM Press, Toronto, Canada (2010).
Quasthoff, M., Meinel, C.: Tracing the Provenance of Object-Oriented Computations on RDF Data.Proceedings of the 2nd Workshop (in conjunction with 7th ESWC) on Trust and Privacy on the Social and Semantic Web (SPOT 2010). p. paper 4. CEUR-WS, Heraklion, Greece (2010).
Quasthoff, M., Meinel, C.: A Linked Data Web Programm Framework.Proceedings of the 7th Extended Semantic Web Conference (ESWC 2010). Springer, Heraklion, Greece (2010).
Baumann, B., Sack, H., Groß, A., Meinel, C.: Linking the tele-TASK Video Portal to the Semantic Web.Proceedings of the 9th International Conference on Innovative Internet Community Systems (I2CS 2009). pp. 205-216. GI LNI Press, Jena, Germany (2009).
Bross, J., Acar, A.E., Schilf, P., Meinel, C.: Spurring Design Thinking through Educational Weblogging.Proceedings of the 2009 Workshop (in conjunction with SocialCom'09) on Computational Science and Engineering (WSCE 2009). pp. 903-908. IEEE Press, Vancouver, Canada (2009).
Quasthoff, M., Sack, H., Meinel, C.: How to Simplify Building Semantic Web Applications.Proceedings of the 5th International Workshop on Semantic Web Enabled Software Engineering (SWESE 2009), in conjunction with the 8th International Semantic Web Conference (ISWC 2009). , Washington, DC, United States (2009).
This paper formalizes several independent approaches on how to develop Semantic Web applications using object-oriented programming languages and Object-Triple Mapping. Using such mapping, Semantic Web applications have been developed up to three times faster compared to traditional Semantic Web software engineering. Results show that at the same time, developer satisfaction has been significantly higher if they used object triple mapping. We present a formal notation of object triple mapping and results of an experimental evaluation clearly showing the benefits of such mapping. The work presented here may one day help to make Semantic Web technologies part of the majority of future applications.
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AbstractThis paper formalizes several independent approaches on how to develop Semantic Web applications using object-oriented programming languages and Object-Triple Mapping. Using such mapping, Semantic Web applications have been developed up to three times faster compared to traditional Semantic Web software engineering. Results show that at the same time, developer satisfaction has been significantly higher if they used object triple mapping. We present a formal notation of object triple mapping and results of an experimental evaluation clearly showing the benefits of such mapping. The work presented here may one day help to make Semantic Web technologies part of the majority of future applications.
Quasthoff, M., Meinel, C.: Design Pattern for Object-Triple Mapping.Proceedings of the 2009 IEEE International Conference on Services Computing (SCC 2009). pp. 443-450. IEEE Press, Bangalore, India (2009).
Broß, J., Quasthoff, M., Zimmermann, J., MacNiven, S., Meinel, C.: Implementing a Corporate Weblog for SAP.Proceedings of the 6th BlogTalk on Recent Trends and Developments in Social Software (BlogTalk 2009). pp. 15-28. Springer, Jeju Island, Korea (2009).
Bross, J., Oppermann, J., Meinel, C.: Enabling Video-blogging without Relying on External Service-providers.Proceedings of IEEE Symposium on Social Computing Applications (SCA 2009). pp. 515-522. IEEE Press, Vancouver, Canada (2009).
Groß, A., Baumann, B., Bross, J., Meinel, C.: Distribution to Multiple Platforms Based on One Video Lecture Archive.Proceedings of the 37th annual ACM SIGUCCS fall conference (SIGUCCS fall 2009). pp. 79-84. ACM Press, St Louis, MO, USA (2009).
Wang, L., Meinel, C.: X-tracking the Changes of Web Navigation Patterns.Proceedings of the 13th International Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009). pp. 772-779. Springer, Bangkok, Thailand (2009).
Yeung, C.-M.A., Noll, M.G., Gibbins, N., Meinel, C., Shadbolt, N.: On Measuring Expertise in Collaborative Tagging Systems.Proceedings of the 1st International Conference on Society On-Line (WebSci 2009). , Athens, Greece (2009).
Noll, M.G., Yeung, C.-M.A., Gibbins, N., Meinel, C., Shadbolt, N.: Telling Experts from Spammers: Expertise Ranking in Folksonomies.Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2009). pp. 612-619. ACM Press, Boston, USA (2009).
Wang, L., Bross, J., Meinel, C.: Post Recommendation in Political Social Web Site.Proceedings of the 8th International Conference on Electronic Government (EGOV 2009). pp. 210-221. Springer, Linz Austria (2009).
Quasthoff, M., Sack, H., Meinel, C.: Can Software Developers Use Linked Data Vocabulary?Proceedings of the 5th International Conference on Semantic Systems (I-SEMANTICS 2009). pp. 542-549. Verlag der TU Graz, Graz, Austria (2009).
Noll, M.G., Meinel, C.: Building a Scalable Collaborative Web Filter with Free and Open Source Software.Proceedings of the 4th IEEE International Conference on Signal-Image Technology & Internet-based Systems (SITIS 2008). pp. 563-571. IEEE CS Press, Bali, Indonesia (2008).
Noll, M.G., Meinel, C.: Exploring Social Annotations for Web Document Classification.Proceedings of the 2008 ACM symposium on Applied computing (SAC 2008). pp. 2315 - 2320. ACM Press, Fortaleza, Ceará, Brazil (2008).
Quasthoff, M., Meinel, C.: Semantic Web Admission Free – Obtaining RDF and OWL Data from Application Source Code.Proceedings of the 4th International Workshop on Semantic Web Enabled Software Engineering (SWESE 2008). , Karlsruhe, Germany (2008).
Quasthoff, M., Sack, H., Meinel, C.: Who Reads and Writes the Social Web? A Security Architecture for Web 2.0 Applications.Proceedings of the 3rd International Conference on Internet and Web Applications and Services (ICIW 2008). pp. 576-582. IEEE Press, Athens (2008).
Noll, M.G., Meinel, C.: The Metadata Triumvirate: Social Annotations, Anchor Texts and Search Queries.Proceedings of the 7th IEEE/WIC/ACM International Conference on Web Intelligence (WI 2008). pp. 640-647. IEEE CS Press, Sydney, Australia (2008).
Bross, J., Meinel, C.: Can VoIP Live up to the QoS Standards of Traditional Wireline Telephony?Proceedings of the 4th Advanced International Conference on Telecommunications (AICT 2008). pp. 126-132. IEEE Press, Athens (2008).
Noll, M.G., Meinel, C.: Authors vs. Readers: A Comparative Study of Document Metadata and Content in the WWW.Proceedings of the 2007 ACM symposium on Document engineering (DocEng 2007). pp. 177 - 186. ACM Press, Winnipeg, Canada (2007).
Noll, M.G., Meinel, C.: Personalization 2.0: Web Search Personlization via Social Bookmarking and Tagging.Proceedings of the 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference (ISWC 2007 + ASWC 2007). pp. 367 - 380. Springer, Busan, South Korea (2007).
Wang, L., Meinel, C.: Detecting the Changes of Web Students' Learning Interest.Proceedings of the 6th International Conference on Web Intelligence (WI 2007). pp. 816 - 819. IEEE Press, Silicon Valley, USA (2007).
Quasthoff, M., Sack, H., Meinel, C.: Why HTTPS Is Not Enough - A Signature-Based Architecture for Trusted Content on the Social Web.Proceedings of the 6th International Conference on Web Intelligence (WI 2007). pp. 820 - 824. IEEE Press, Silicon Valley, USA (2007).
Wang, L., Meinel, C.: Mining the Students' Learning Interest in Browsing Web-Streaming Lectures.Proceedings of IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2007). pp. 194 - 201. IEEE Press, Honolulu, USA (2007).
Haffner, E.-G., Heuer, A., Roth, U., Engel, T., Meinel, C.: Advanced Studies on Link Proposals and Knowledge Retrieval of Hypertexts with CBR.Proceedings of the first International Conference on Electronic Commerce and Web Technologies (EC-Web 2000). pp. 369-378. Springer, Grennwich, United Kingdom (2000).
Heuer, A., Haffner, E.-G., Roth, U., Meinel, C.: A Hyperlink Focused Browse Assistent for the World Wide Web.Proceedings of the 1st International Conference on Internet Computing (IC 2000). pp. 79-84. CSREA Press, Las Vegas, USA (2000).
Qu, C., Engel, T., Meinel, C.: Implementation of a Document Management System Based on WebDAV Protocol.Proceedings of the 2000 IEEE International Conference on Management of Innovation and Technology (ICMIT 2000). pp. 752 - 757. IEEE Press, Singapore, Malaysia (2000).
Qu, C., Engel, T., Meinel, C.: Implementation of an Enterprise-level Groupware System Based on J2EE Platform and WebDAV Protocol.Proceedings of the 4th InternationalEnterprise Distributed Object Computing Conference (EDOC 2000). pp. 160 - 169. IEEE Press, Makuhari, Japan (2000).
Haffner, E.-G., Roth, U., Heuer, A., Engel, T., Meinel, C.: Link Proposals with Case-Based Reasoning Techniques.Proceedings of WebNet 2000 - World Conference on the WWW and Internet (WebNet 2000). pp. 233-239. AACE, San Antonio, Texas, USA (2000).
Zhang, Z., Roth, U., Engel, T., Meinel, C.: Web Site Design Using a Web-Based Authoring and Publishing System.Proceedings of the 1st International Conference on Internet Computing (IC 2000). pp. 113-118. CSREA Press, Las Vegas, USA (2000).
Qu, C., Engel, T., Meinel, C.: Implementation of a WebDAV-based Collaborative Distance Learning Environment.Proceedings of the 28th SIGUCCS Conference on User Services (SIGUCCS 2000). pp. 258-265. ACM Press, Richmond, Virginia, USA (2000).
Haffner, E.-G., Roth, U., Heuer, A., Engel, T., Meinel, C.: What do Hyperlink-Proposals and Request-Prediction have in Common?Proceedings of the First International Conference on Advances in Information Systems (ADVIS 2000). pp. 285-293. Springer, Turkey (2000).
Heuer, A., Losemann, F., Meinel, C.: Logging and Signing Document Transfers on the WWW - A Trusted Third Party Gateway.Proceedings of the First International Conference on Web Information Systems Engineering (WISE 2000). pp. 146 - 152. IEEE Press, Hong Kong, China (2000).
Roth, U., Heuer, A., Haffner, E.-G., Meinel, C.: A Search-Engine-Topology to Improve Document Retrieval on the Web.Proceedings of WebNet 2000 - World Conference on the WWW and Internet, San Antonio (WebNet 2000). pp. 470-475. AACE, San Antonio, Texas, USA (2000).
Roth, U., Engel, T., Meinel, C.: Improving the Quality of Information-Flow with the Smart Data Server.Proceedings of the International Conference on Internet Computing (IC 2000). pp. 353-357. CSREA Press, Las Vegas, Nevada, USA (2000).
Haffner, E.-G., Roth, U., Heuer, A., Engel, T., Meinel, C.: Advanced Techniques for Analyzing Web Server Logs.Proceedings of the 1st International Conference on Internet Computing (IC 2000). pp. 71-78. CSREA Press, Las Vegas, USA (2000).
