Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes
Summary: SCARE uses maximal likelihood under learned distribution to select replacements; a metric balances gain and changes. Horizontal partitioning with local-to-global aggregation enables scalable, bounded-change repairs; experiments show efficiency. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mohamed Yakout (Microsoft)
- 2. Laure Berti-Équille (Institut de Recherche pour le Développement)
- 3. Ahmed K. Elmagarmid (Qatar Computing Research Institute)
BibTeX Citation
@inproceedings{yakout_sigmod13,
title = {{Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes}},
author = {Yakout, Mohamed and Berti-Équille, Laure and Elmagarmid, Ahmed K.},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2463706},
url = {https://dl.acm.org/doi/10.1145/2463676.2463706},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 494 | Dependencies Revisited for Improving Data Quality | 2008 | PODS | 0.00017526549 |
| 533 | Improving Data Quality: Consistency and Accuracy | 2007 | VLDB | 0.0001705859 |
| 549 | ERACER: A Database Approach for Statistical Inference and Data Cleaning | 2010 | SIGMOD | 0.00016692839 |
| 714 | Guided Data Repair | 2011 | VLDB | 0.00014662041 |
| 998 | Towards Certain Fixes with Editing Rules and Master Data | 2010 | VLDB | 0.0001275238 |
| 2,782 | Interaction between Record Matching and Data Repairing | 2011 | SIGMOD | 8.1280264e-05 |
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