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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
h5413f799ac4e77d2
Venue
SIGMOD
Year
2019
Pagerank
8.0610697e-05
Overall Rank
2,748 | 81.53%
DOI
10.1145/3299869.3319855
Incoming Non-self Citations Over Time
BibTeX Citation
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@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
89
WebTables: Exploring the Power of Tables on the Web
2008
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95
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2001
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2017
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CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies
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A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification
2005
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350
Discovering Denial Constraints
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2002
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2007
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1998
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Discovery of Genuine Functional Dependencies from Relational Data with Missing Values
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