The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Conditional Functional Dependencies for Data Cleaning
Open accessAbstract
We propose a class of constraints, referred to as conditional functional dependencies (CFDs), and study their applications in data cleaning. In contrast to traditional functional dependencies (FDs) that were developed mainly for schema design, CFDs aim at capturing the consistency of data by incorporating bindings of semantic ally related values. For CFDs we provide an inference system analogous to Armstrong's axioms for FDs, as well as consistency analysis. Since CFDs allow data bindings, a large number of individual constraints may hold on a table, complicating detection of constraint violations. We develop techniques for detecting CFD violations in SQL as well as novel techniques for checking multiple constraints in a single query. We experimentally evaluate the performance of our CFD-based methods for inconsistency detection. This not only yields a constraint theory for CFDs but is also a step toward a practical constraint-based method for improving data quality.
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Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
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Potter's Wheel: An Interactive Data Cleaning System
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A cost-based model and effective heuristic for repairing constraints by value modification
Bohannon, P., Fan, W., Flaster, M., & Rastogi, R. (2005) · Proceedings of the 2005 ACM SIGMOD international conference on Management of data · doi:10.1145/1066157.1066175 · Cited by 424
Self-citationMinimal-change integrity maintenance using tuple deletions
Chomicki, J., & Marcinkowski, J. (2005) · Information and Computation · doi:10.1016/j.ic.2004.04.007 · Cited by 347
AJAX: an extensible data cleaning tool
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Cited by
Data Cleaning: Overview and Emerging Challenges
Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016) · Proceedings of the 2016 International Conference on Management of Data · doi:10.1145/2882903.2912574 · Cited by 488
HoloClean: holistic data repairs with probabilistic inference
Rekatsinas, T., Chu, X., Ilyas, I. F., & Ré, C. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137628.3137631 · Cited by 480
Proceedings of the 19th Python in Science Conference
(2020) · Proceedings of the Python in Science Conference · doi:10.25080/majora-342d178e-02b · Cited by 284
Data quality: The other face of Big Data
Saha, B., & Srivastava, D. (2014) · 2014 IEEE 30th International Conference on Data Engineering · doi:10.1109/icde.2014.6816764 · Cited by 262
Metadata
- Authors
- Bohannon, P., Fan, W., Geerts, F., Jia, X., & Kementsietsidis, A.
- Venue
- 2007 IEEE 23rd International Conference on Data Engineering
- Publisher
- IEEE
- Published
- 2007-04
- Pages
- 746-755
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2153531471
- Cited by
- 375
- References
- 14
- FWCI
- 28.36
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bohannon, P., Fan, W., Geerts, F., Jia, X., & Kementsietsidis, A. (2007). Conditional Functional Dependencies for Data Cleaning. 2007 IEEE 23rd International Conference on Data Engineering. https://doi.org/10.1109/icde.2007.367920
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