Profiling relational data: a survey
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778_2015_Article_389.pdf
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1.03 MB
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Adobe PDF
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8418abe9049dc55f77eca52dc12bb38e
Author(s)
Abedjan, Ziawasch
Golab, Lukasz
Naumann, Felix
Date Issued
June 2015
Journal
The VLDB Journal
Publisher
Springer Berlin Heidelberg
Citation
Abedjan, Ziawasch, Lukasz Golab, and Felix Naumann. “Profiling Relational Data: A Survey.” The VLDB Journal 24.4 (2015): 557–581.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s00778-015-0389-y
