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Cleaning Denial Constraint Violations through Relaxation
Summary: On-demand probabilistic repair of denial-constraint violations embedded in analytics. Daisy weaves cleaning operators into query plans, relaxing results over dirty data to adapt to workload, outperforming offline cleaning on synthetic and real data.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hb25118c3be91a233
Venue
SIGMOD
Year
2020
Pagerank
7.3488827e-05
Overall Rank
3,372 | 77.40%
DOI
10.1145/3318464.3389775
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{giannakopoulou_sigmod20,
title = {{Cleaning Denial Constraint Violations through Relaxation}},
author = {Giannakopoulou, Stella and Karpathiotakis, Manos and Ailamaki, Anastasia},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389775},
url = {https://dl.acm.org/doi/10.1145/3318464.3389775},
year = {2020}
}
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ActiveClean: Interactive Data Cleaning For Statistical Modeling
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Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes
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NADEEF: A Commodity Data Cleaning System
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The LLUNATIC Data-Cleaning Framework
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