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Uni-Detect: A Unified Approach to Automated Error Detection in Tables
Summary: UniDetect: unified error detection for tables using what-if perturbations and cross-table hypothesis tests, no per-dataset tuning required. It finds FD violations, numeric outliers, spelling mistakes, and outperforms specialized methods.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
5729
Venue
SIGMOD
Year
2019
Pagerank
8.1995954e-05
Overall Rank
2,728 | 81.29%
DOI
10.1145/3299869.3319855
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@inproceedings{wang_sigmod19,
title = {{Uni-Detect: A Unified Approach to Automated Error Detection in Tables}},
author = {Wang, Pei and He, Yeye},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319855},
url = {https://dl.acm.org/doi/10.1145/3299869.3319855},
year = {2019}
}
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Showing 27 of 27 cited papers.
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Pagerank
91
WebTables: Exploring the Power of Tables on the Web
2008
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94
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2001
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CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies
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533
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Transform-Data-by-Example (TDE): An Extensible Search Engine for Data Transformations
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