Discovery of Genuine Functional Dependencies from Relational Data with Missing Values
Summary: Defines genuineness of FDs under missing-value semantics; proposes algorithms to compute a genuineness score for discovered FDs. Extensive experiments on real and semi-synthetic data show accurate extraction of genuine FDs from large FD sets, aiding robust data cleaning and repairs. (summarized by gpt-5-nano on Feb 09 2026)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,047 | Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms | 2015 | VLDB | 0.00014459715 |
| 1,159 | Towards Certain Fixes with Editing Rules and Master Data | 2010 | VLDB | 0.00013592813 |
| 1,624 | Sampling the Repairs of Functional Dependency Violations under Hard Constraints | 2010 | VLDB | 0.00011099222 |
| 1,730 | Conditioning Probabilistic Databases | 2008 | VLDB | 0.00010736755 |
| 4,499 | Possible and Certain SQL Keys | 2015 | VLDB | 6.1385333e-05 |
| 5,253 | Enriching Data Imputation with Extensive Similarity Neighbors | 2015 | VLDB | 5.6014916e-05 |
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