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Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints

Summary: Contingency analysis under predicate-constraints bounds aggregates (SUM, COUNT, AVG, MIN, MAX) with missing data. An integer-program optimizer reconciles overlapping constraints to yield guaranteed hard bounds; experiments show competitive accuracy vs baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6062
Venue
SIGMOD
Year
2020
Pagerank
5.5085906e-05
Overall Rank
8,002 | 45.10%
DOI
10.1145/3318464.3389785

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{liang_sigmod20,
        title = {{Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints}},
        author = {Liang, Xi and Shang, Zechao and Krishnan, Sanjay and Elmore, Aaron J. and Franklin, Michael J.},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3389785},
        url = {https://dl.acm.org/doi/10.1145/3318464.3389785},
        year = {2020}
}

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