The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Holistic data cleaning: Putting violations into context
Abstract
Data cleaning is an important problem and data quality rules are the most promising way to face it with a declarative approach. Previous work has focused on specific formalisms, such as functional dependencies (FDs), conditional functional dependencies (CFDs), and matching dependencies (MDs), and those have always been studied in isolation. Moreover, such techniques are usually applied in a pipeline or interleaved. In this work we tackle the problem in a novel, unified framework. First, we let users specify quality rules using denial constraints with ad-hoc predicates. This language subsumes existing formalisms and can express rules involving numerical values, with predicates such as “greater than” and “less than”. More importantly, we exploit the interaction of the heterogeneous constraints by encoding them in a conflict hypergraph. Such holistic view of the conflicts is the starting point for a novel definition of repair context which allows us to compute automatically repairs of better quality w.r.t. previous approaches in the literature. Experimental results on real datasets show that the holistic approach outperforms previous algorithms in terms of quality and efficiency of the repair.
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Cites
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Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
Duplicate Record Detection: A Survey
Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.250581 · Cited by 1,267
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Conditional functional dependencies for capturing data inconsistencies
Fan, W., Geerts, F., Jia, X., & Kementsietsidis, A. (2008) · ACM Transactions on Database Systems · doi:10.1145/1366102.1366103 · Cited by 460
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
Improving data quality: consistency and accuracy
Cong, G., Fan, W., Geerts, F., et al. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 313
Discovering data quality rules
Chiang, F., & Miller, R. J. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453980 · Cited by 265
Reasoning about record matching rules
Fan, W., Jia, X., Li, J., & Ma, S. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687674 · Cited by 226
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Cited by
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445518 · Cited by 781
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
Self-citationHoloClean: 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
Self-citationData 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
Data Lifecycle Challenges in Production Machine Learning: A Survey
Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2018) · ACM SIGMOD Record · doi:10.1145/3299887.3299891 · Cited by 214
Metadata
- Authors
- Chu, X., Ilyas, I. F., & Papotti, P.
- Venue
- 2013 IEEE 29th International Conference on Data Engineering (ICDE)
- Publisher
- IEEE
- Published
- 2013-04
- Pages
- 458-469
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2044469685
- Cited by
- 326
- References
- 18
- FWCI
- 31.30
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chu, X., Ilyas, I. F., & Papotti, P. (2013). Holistic data cleaning: Putting violations into context. 2013 IEEE 29th International Conference on Data Engineering (ICDE). https://doi.org/10.1109/icde.2013.6544847
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
