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)
Showing 37 of 137 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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