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ANMAT: Automatic Knowledge Discovery and Error Detection through Pattern Functional Dependencies

Summary: PFDs fuse patterns with integrity constraints to model partial-value dependencies across attributes, extending FDs. ANMAT automates PFD discovery on dirty data and flags errors via PFD violations, catching issues missed by existing approaches. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5779
Venue
SIGMOD
Year
2019
Pagerank
6.2923403e-05
Overall Rank
5,268 | 63.86%
DOI
10.1145/3299869.3320209

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{qahtan_sigmod19,
        title = {{ANMAT: Automatic Knowledge Discovery and Error Detection through Pattern Functional Dependencies}},
        author = {Qahtan, Abdulhakim and Tang, Nan and Ouzzani, Mourad and Cao, Yang and Stonebraker, Michael},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3320209},
        url = {https://dl.acm.org/doi/10.1145/3299869.3320209},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

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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
2,398 BigDansing: A System for Big Data Cleansing 2015 SIGMOD 8.631172e-05
3,147 Auto-Detect: Data-Driven Error Detection in Tables 2018 SIGMOD 7.7077175e-05
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