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)
Showing 39 of 39 citing papers.
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 500 | Dependencies Revisited for Improving Data Quality | 2008 | PODS | 0.00017280722 |
| 526 | Improving Data Quality: Consistency and Accuracy | 2007 | VLDB | 0.00016886621 |
| 547 | ERACER: A Database Approach for Statistical Inference and Data Cleaning | 2010 | SIGMOD | 0.00016578131 |
| 716 | Guided Data Repair | 2011 | VLDB | 0.00014553463 |
| 989 | Towards Certain Fixes with Editing Rules and Master Data | 2010 | VLDB | 0.0001265344 |
| 2,784 | Interaction between Record Matching and Data Repairing | 2011 | SIGMOD | 8.018482e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 716 | Guided Data Repair | 2011 | VLDB |
| 2 | 11,661 | Generalizable Data Cleaning of Tabular Data in Latent Space | 2024 | VLDB |
| 3 | 1,043 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD |
| 4 | 10,529 | Minimum Change ≠ Best Cleaning: Parallel and Incremental Error Detection under Integrity Constraints | 2026 | SIGMOD |
| 5 | 2,784 | Interaction between Record Matching and Data Repairing | 2011 | SIGMOD |
| 6 | 9,696 | Constraint-Variance Tolerant Data Repairing | 2016 | SIGMOD |
| 7 | 104 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB |
| 8 | 2,964 | Towards Dependable Data Repairing with Fixing Rules | 2014 | SIGMOD |
| 9 | 3,426 | Modeling and Querying Possible Repairs in Duplicate Detection | 2009 | VLDB |
| 10 | 3,263 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB |