Automatic Data Repair: Are We Ready to Deploy?
Summary: A taxonomy-driven study benchmarks 12 data-repair algorithms across 12 datasets, error conditions, and four downstream tasks using a practical error-reduction metric. A unified repair optimization strategy improves state-of-the-art methods, showing repair remains beneficial—even clean data is not the performance ceiling. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wei Ni (City University of Hong Kong; Zhejiang University)
- 2. Xiaoye Miao (Zhejiang University)
- 3. Xiangyu Zhao (City University of Hong Kong)
- 4. Yangyang Wu (Zhejiang University)
- 5. Shuwei Liang (Zhejiang University)
- 6. Jianwei Yin (Zhejiang University)
BibTeX Citation
@article{ni_vldb24,
title = {{Automatic Data Repair: Are We Ready to Deploy?}},
author = {Ni, Wei and Miao, Xiaoye and Zhao, Xiangyu and Wu, Yangyang and Liang, Shuwei and Yin, Jianwei},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2617--2630},
doi = {10.14778/3675034.3675051},
url = {https://doi.org/10.14778/3675034.3675051},
year = {2024}
}
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