INCA: Inconsistency-Aware Data Profiling and Querying
Summary: INCA annotates inconsistencies with why-provenance and provenance polynomials against denial constraints, enabling provenance-based profiling. 4 inconsistency measures under bag/set semantics; supports top-k and threshold queries for selective cleaning. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ousmane ISSA (University Clermont Auvergne)
- 2. Angela Bonifati (Claude Bernard Lyon 1 University)
- 3. Farouk Toumani (University Clermont Auvergne)
BibTeX Citation
@inproceedings{issa_sigmod21,
title = {{INCA: Inconsistency-Aware Data Profiling and Querying}},
author = {ISSA, Ousmane and Bonifati, Angela and Toumani, Farouk},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452760},
url = {https://dl.acm.org/doi/10.1145/3448016.3452760},
year = {2021}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 17 | Provenance Semirings | 2007 | PODS | 0.00059843817 |
| 3,387 | Data Profiling – A Tutorial | 2017 | SIGMOD | 7.4534663e-05 |
| 8,133 | Evaluating Top-k Queries with Inconsistency Degrees | 2020 | VLDB | 5.4813895e-05 |
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