HoloClean: Holistic Data Repairs with Probabilistic Inference
Summary: HoloClean couples constraint-driven and statistical data repair via automatic probabilistic-program generation from dirty data. Scalable inference over millions of tuples; precision ~90%, recall ~76%, F1 >2x vs state-of-the-art. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Theodoros Rekatsinas
- 2. Xu Chu
- 3. Ihab F. Ilyas
- 4. Christopher Ré
Incoming Citations (Sorted by Pagerank)
Showing 33 of 133 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| Overall Rank | Paper | Year | Venue | Pagerank |
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| 1,627 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD | 0.00011086905 |
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| 881 | Don’t be SCAREd: Use SCalable Automatic REpairing with Maximal Likelihood and Bounded Changes | 2013 | SIGMOD | 0.00015661103 |
| 2,823 | Interaction between Record Matching and Data Repairing | 2011 | SIGMOD | 8.0593894e-05 |
| 9,369 | Constraint-Variance Tolerant Data Repairing | 2016 | SIGMOD | 4.3481081e-05 |
| 1,337 | HoloDetect: Few-Shot Learning for Error Detection | 2019 | SIGMOD | 0.00012497164 |
| 3,396 | Automatic Data Repair: Are We Ready to Deploy? | 2024 | VLDB | 7.1455126e-05 |
| 5,153 | Horizon: Scalable Dependency-driven Data Cleaning | 2021 | VLDB | 5.6607963e-05 |
| 3,192 | Towards Dependable Data Repairing with Fixing Rules | 2014 | SIGMOD | 7.4095761e-05 |