Analyzing Deviations from Monotonic Trends through Database Repair
Summary: Introduces Aggregate Order Dependencies (AODs), an aggregation-centric extension of order dependencies for measuring violations of expected monotonic trends in data. Casts AOD repair as minimum tuple deletion, gives complexity + generic/optimized algorithms and heuristics, and uses them to explain real trend deviations. (summarized by gpt-5.4-mini on Apr 11 2026)
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Authors
- 1. Shunit Agmon (Technion)
- 2. Jonathan Gal (Technion)
- 3. Amir Gilad (Hebrew University)
- 4. Ester Livshits (Technion)
- 5. Or Mutay (Technion)
- 6. Brit Youngmann (Technion)
- 7. Benny Kimelfeld (Technion)
BibTeX Citation
@inproceedings{agmon_sigmod26,
title = {{Analyzing Deviations from Monotonic Trends through Database Repair}},
author = {Agmon, Shunit and Gal, Jonathan and Gilad, Amir and Livshits, Ester and Mutay, Or and Youngmann, Brit and Kimelfeld, Benny},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786624},
url = {https://dl.acm.org/doi/10.1145/3786624},
year = {2026}
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