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Dependencies Revisited for Improving Data Quality
Summary: Survey of modern revisions to classical dependency theory tailored for data quality: covers approximate, conditional, probabilistic and matching-aware variants that better model real-world inconsistencies. Emphasizes their use for detection, matching, repair and querying of inconsistent data and outlines research directions for theory and systems.
(summarized by gpt-5-mini on Feb 09 2026)
Paper ID
hb05159b12e2af120
Venue
PODS
Year
2008
Pagerank
0.00017280722
Overall Rank
500 | 96.64%
DOI
10.1145/1376916.1376940
Incoming Non-self Citations Over Time
BibTeX Citation
@inproceedings{fan_pods08,
address = {New York, NY, USA},
series = {{PODS} '08},
title = {{Dependencies Revisited for Improving Data Quality}},
url = {https://dl.acm.org/doi/10.1145/1376916.1376940},
doi = {10.1145/1376916.1376940},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Fan, Wenfei and Fan, Wenfei},
year = {2008}
}
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7.8052551e-05
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Scalable Discovery of Unique Column Combinations 2014
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Efficient Differential Dependency Discovery 2024
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Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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