PIClean: A Probabilistic and Interactive Data Cleaning System
Summary: PIClean is a probabilistic, interactive data cleaning system that uses low-rank approximation to uncover cross-column relationships for joint error detection and repair. User feedback confirms or rejects probabilistic fixes, continually updating models to improve accuracy and coverage. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhuoran Yu (Georgia Institute of Technology)
- 2. Xu Chu (Georgia Institute of Technology)
BibTeX Citation
@inproceedings{yu_sigmod19,
title = {{PIClean: A Probabilistic and Interactive Data Cleaning System}},
author = {Yu, Zhuoran and Chu, Xu},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3320214},
url = {https://dl.acm.org/doi/10.1145/3299869.3320214},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,954 | Semi-Supervised Data Cleaning with Raha and Baran | 2021 | CIDR | 5.9357922e-05 |
| 7,250 | Akane: Perplexity-Guided Time Series Data Cleaning | 2024 | SIGMOD | 5.574104e-05 |
| 10,687 | SHoTClean: Bridging Soft and Hard Constraints for Multivariate Time Series Cleaning | 2026 | SIGMOD | 4.9793485e-05 |
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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 |
|---|---|---|---|---|
| 95 | Potter's Wheel: An Interactive Data Cleaning System | 2001 | VLDB | 0.00034382643 |
| 104 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00033690989 |
| 350 | Discovering Denial Constraints | 2013 | VLDB | 0.00020253521 |
| 649 | Discovering Data Quality Rules | 2008 | VLDB | 0.00015149865 |
| 1,043 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD | 0.00012335114 |
| 1,344 | Detecting Data Errors: Where are we and what needs to be done? | 2016 | VLDB | 0.00010956518 |
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|---|---|---|---|---|
| 1 | 4,998 | Descriptive and Prescriptive Data Cleaning | 2014 | SIGMOD |
| 2 | 8,695 | From Papers to Practice: The openclean Open-Source Data Cleaning Library | 2021 | VLDB |
| 3 | 6,381 | Qualitative Data Cleaning | 2016 | VLDB |
| 4 | 483 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB |
| 5 | 1,043 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD |
| 6 | 4,960 | PrivateClean: Data Cleaning and Differential Privacy | 2016 | SIGMOD |
| 7 | 104 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB |
| 8 | 12,177 | IHCS: An Integrated Hybrid Cleaning System | 2019 | VLDB |
| 9 | 5,881 | ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning | 2016 | SIGMOD |
| 10 | 9,470 | VisClean: Interactive Cleaning for Progressive Visualization | 2020 | VLDB |