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}
}
Incoming Citations (Sorted by Pagerank)
Showing 50 of 51 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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