2021

1.
Harmouch, H., Papenbrock, T., Naumann, F.: Relational Header Discovery using Similarity Search in a Table Corpus. IEEE International Conference on Data Engineering (ICDE). 444–455 (2021).
[ Abstract ] [ BibTeX ] [ Details ]
 
Column headers are among the most relevant types of meta-data for relational tables, because they provide meaning and context in which the data is to be interpreted. Headers play an important role in many data integration, exploration, and cleaning scenarios, such as schema matching, knowledge base augmentation, and similarity search. Unfortunately, in many cases column headers are missing, because they were never defined properly, are meaningless, or have been lost during data extraction, transmission, or storage. For example, around one third of the tables on the Web have missing headers. Missing headers leave abundant tabular data shrouded and inaccessible to many data-driven applications. We introduce a fully automated, multi-phase system that discovers table column headers for cases where headers are missing, meaningless, or unrepresentative for the column values. It leverages existing table headers from web tables to suggest human-understandable, representative, and consistent headers for any target table. We evaluate our system on tables extracted from Wikipedia. Overall, 60% of the automatically discovered table headers are exact and complete. Considering more header candidates, top-5 for example, increases this percentage to 72%.
@article{harmouch2021relational, abstract = {Column headers are among the most relevant types of meta-data for relational tables, because they provide meaning and context in which the data is to be interpreted. Headers play an important role in many data integration, exploration, and cleaning scenarios, such as schema matching, knowledge base augmentation, and similarity search. Unfortunately, in many cases column headers are missing, because they were never defined properly, are meaningless, or have been lost during data extraction, transmission, or storage. For example, around one third of the tables on the Web have missing headers. Missing headers leave abundant tabular data shrouded and inaccessible to many data-driven applications. We introduce a fully automated, multi-phase system that discovers table column headers for cases where headers are missing, meaningless, or unrepresentative for the column values. It leverages existing table headers from web tables to suggest human-understandable, representative, and consistent headers for any target table. We evaluate our system on tables extracted from Wikipedia. Overall, 60% of the automatically discovered table headers are exact and complete. Considering more header candidates, top-5 for example, increases this percentage to 72%.}, author = {Harmouch, Hazar and Papenbrock, Thorsten and Naumann, Felix}, journal = {IEEE International Conference on Data Engineering (ICDE)}, keywords = {myown data_profiling data_sketches similarity_search isg header_discovery}, pages = {444-455}, title = {Relational Header Discovery using Similarity Search in a Table Corpus}, year = 2021 }
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AbstractColumn headers are among the most relevant types of meta-data for relational tables, because they provide meaning and context in which the data is to be interpreted. Headers play an important role in many data integration, exploration, and cleaning scenarios, such as schema matching, knowledge base augmentation, and similarity search. Unfortunately, in many cases column headers are missing, because they were never defined properly, are meaningless, or have been lost during data extraction, transmission, or storage. For example, around one third of the tables on the Web have missing headers. Missing headers leave abundant tabular data shrouded and inaccessible to many data-driven applications. We introduce a fully automated, multi-phase system that discovers table column headers for cases where headers are missing, meaningless, or unrepresentative for the column values. It leverages existing table headers from web tables to suggest human-understandable, representative, and consistent headers for any target table. We evaluate our system on tables extracted from Wikipedia. Overall, 60% of the automatically discovered table headers are exact and complete. Considering more header candidates, top-5 for example, increases this percentage to 72%.

2019

1.
Dürsch, F., Stebner, A., Windheuser, F., Fischer, M., Friedrich, T., Strelow, N., Bleifuß, T., Harmouch, H., Jiang, L., Papenbrock, T., Naumann, F.: Inclusion Dependency Discovery: An Experimental Evaluation of Thirteen Algorithms. Proceedings of the International Conference on Information and Knowledge Management (CIKM). pp. 219–228 (2019).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Inclusion dependencies are an important type of metadata in relational databases, because they indicate foreign key relationships and serve a variety of data management tasks, such as data linkage, query optimization, and data integration. The discovery of inclusion dependencies is, therefore, a well-studied problem and has been addressed by many algorithms. Each of these discovery algorithms follows its own strategy with certain strengths and weaknesses, which makes it difficult for data scientists to choose the optimal algorithm for a given profiling task. This paper summarizes the different state-of-the-art discovery approaches and discusses their commonalities. For evaluation purposes, we carefully re-implemented the thirteen most popular discovery algorithms and discuss their individual properties. Our extensive evaluation on several real-world and synthetic datasets shows the unbiased performance of the different discovery approaches and, hence, provides a guideline on when and where each approach works best. Comparing the different runtimes and scalability graphs, we identify the best approaches for certain situations and demonstrate where certain algorithms fail.
@inproceedings{dursch2019inclusion, abstract = {Inclusion dependencies are an important type of metadata in relational databases, because they indicate foreign key relationships and serve a variety of data management tasks, such as data linkage, query optimization, and data integration. The discovery of inclusion dependencies is, therefore, a well-studied problem and has been addressed by many algorithms. Each of these discovery algorithms follows its own strategy with certain strengths and weaknesses, which makes it difficult for data scientists to choose the optimal algorithm for a given profiling task. This paper summarizes the different state-of-the-art discovery approaches and discusses their commonalities. For evaluation purposes, we carefully re-implemented the thirteen most popular discovery algorithms and discuss their individual properties. Our extensive evaluation on several real-world and synthetic datasets shows the unbiased performance of the different discovery approaches and, hence, provides a guideline on when and where each approach works best. Comparing the different runtimes and scalability graphs, we identify the best approaches for certain situations and demonstrate where certain algorithms fail.}, author = {Dürsch, Falco and Stebner, Axel and Windheuser, Fabian and Fischer, Maxi and Friedrich, Tim and Strelow, Nils and Bleifuß, Tobias and Harmouch, Hazar and Jiang, Lan and Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM)}, keywords = {isg}, pages = {219–228}, title = {Inclusion Dependency Discovery: An Experimental Evaluation of Thirteen Algorithms}, year = 2019 }
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AbstractInclusion dependencies are an important type of metadata in relational databases, because they indicate foreign key relationships and serve a variety of data management tasks, such as data linkage, query optimization, and data integration. The discovery of inclusion dependencies is, therefore, a well-studied problem and has been addressed by many algorithms. Each of these discovery algorithms follows its own strategy with certain strengths and weaknesses, which makes it difficult for data scientists to choose the optimal algorithm for a given profiling task. This paper summarizes the different state-of-the-art discovery approaches and discusses their commonalities. For evaluation purposes, we carefully re-implemented the thirteen most popular discovery algorithms and discuss their individual properties. Our extensive evaluation on several real-world and synthetic datasets shows the unbiased performance of the different discovery approaches and, hence, provides a guideline on when and where each approach works best. Comparing the different runtimes and scalability graphs, we identify the best approaches for certain situations and demonstrate where certain algorithms fail.

2018

1.
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).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
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 }
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URNhttp://dx.doi.org/10.14778/3204028.3204032
AbstractFunctional 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.

2017

1.
Harmouch, H., Naumann, F.: Cardinality Estimation: An Experimental Survey. Proceedings of the VLDB Endowment (PVLDB). pp. 499–512 (2017).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
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 }
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URNhttp://dx.doi.org/https://doi.org/10.1145/3164135.3164145
AbstractData 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

2016

1.
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 ] [ Details ]
 
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 }
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AbstractToday’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.