UniClean: A Scalable Data Cleaning Solution for Mixed Errors based on Unified Cleaners and Optimized Cleaning Workflow
Summary: UniClean unifies heterogeneous cleaners for scalable mixed-error repair, with optimized preparation and cleaning workflows. It achieves substantial quality gains and hour-scale processing on million-record datasets, improving over prior methods by 30–40%. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Xiaoou Ding (Harbin Engineering University)
- 2. Zekai Qian (Harbin Engineering University)
- 3. Hongzhi Wang (Harbin Engineering University)
- 4. Siying Chen (Harbin Engineering University)
- 5. Yafeng Tang (Harbin Engineering University)
- 6. Hongbin Su (Harbin Engineering University)
- 7. Huan Hu (Huawei Cloud Computing Technologies Co., Ltd.)
- 8. Chen Wang (Tsinghua University)
BibTeX Citation
@article{ding_vldb25,
title = {{UniClean: A Scalable Data Cleaning Solution for Mixed Errors based on Unified Cleaners and Optimized Cleaning Workflow}},
author = {Ding, Xiaoou and Qian, Zekai and Wang, Hongzhi and Chen, Siying and Tang, Yafeng and Su, Hongbin and Hu, Huan and Wang, Chen},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4117--4130},
doi = {10.14778/3749646.3749681},
url = {https://doi.org/10.14778/3749646.3749681},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,079 | bNDCRepair: Cleaning both Data Errors and Inaccurate Constraints on Numerical Sequential Data | 2025 | VLDB | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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