Detecting Data Errors: Where are we and what needs to be done?
Summary: Empirical study: data-cleaning tools miss large portions of real-world errors and have robustness gaps. Proposes multi-tool workflow to boost coverage with less verification; notes domain-specific tools and enrichment, but some errors remain undetectable. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ziawasch Abedjan (Massachusetts Institute of Technology)
- 2. Xu Chu (University of Waterloo)
- 3. Dong Deng (Tsinghua University)
- 4. Raul Castro Fernandez (Massachusetts Institute of Technology)
- 5. Ihab F. Ilyas (University of Waterloo)
- 6. Mourad Ouzzani (Qatar Computing Research Institute)
- 7. Paolo Papotti (Arizona State University)
- 8. Michael Stonebraker (Massachusetts Institute of Technology)
- 9. Nan Tang (Qatar Computing Research Institute)
BibTeX Citation
@article{abedjan_vldb16,
title = {{Detecting Data Errors: Where are we and what needs to be done?}},
author = {Abedjan, Ziawasch and Chu, Xu and Deng, Dong and Fernandez, Raul Castro and Ilyas, Ihab F. and Ouzzani, Mourad and Papotti, Paolo and Stonebraker, Michael and Tang, Nan},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {12},
pages = {993--1004},
doi = {10.14778/2994509.2994518},
url = {https://doi.org/10.14778/2994509.2994518},
year = {2016}
}
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