The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Profiling relational data: a survey
Open accessAbstract
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.
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Cited by
Information Quality Assessment for Data Fusion Systems
Becerra, M. A., Tobón, C., Castro-Ospina, A. E., & Peluffo-Ordóñez, D. H. (2021) · Data · doi:10.3390/data6060060 · Cited by 36
A Survey of Data Quality Measurement and Monitoring Tools
Ehrlinger, L., & Wöß, W. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.850611 · Cited by 25
Metadata
- Authors
- Abedjan, Z., Golab, L., & Naumann, F.
- Venue
- The VLDB Journal
- Published
- 2015-06-02
- Volume
- 24
- Issue
- 4
- Pages
- 557-581
- ISSN
- 0949-877X (ISSN-L)
- 1066-8888
- OpenAlex
- W760598031
- Cited by
- 283
- References
- 89
- FWCI
- 33.00
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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