HoloClean: Holistic Data Repairs with Probabilistic Inference
Summary: HoloClean unifies constraint-/external-source-based and statistical data repair through automatically generated probabilistic programs. Scalable inference optimizations handle millions of tuples, achieving ~90% precision, >76% recall, and >2× F1 over prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Theodoros Rekatsinas (Stanford University)
- 2. Xu Chu (University of Waterloo)
- 3. Ihab F. Ilyas (University of Waterloo)
- 4. Christopher Ré (Stanford University)
BibTeX Citation
@article{rekatsinas_vldb17,
title = {{HoloClean: Holistic Data Repairs with Probabilistic Inference}},
author = {Rekatsinas, Theodoros and Chu, Xu and Ilyas, Ihab F. and Ré, Christopher},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {11},
pages = {1190--1203},
doi = {10.14778/3137628.3137663},
url = {https://doi.org/10.14778/3137628.3137663},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
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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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