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Discovery and Ranking of Embedded Uniqueness Constraints

Summary: First study of embedded uniqueness constraints (eUCs): unique column combinations embedded in complete fragments of incomplete data, realized as filtered indexes for integrity and query optimization. Shows NP-complete decision variant, W[2]-complete, max-size bounds, scalable column/row algorithms with a hybrid approach, and ranking to identify relevant eUCs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12143
Venue
VLDB
Year
2019
Pagerank
5.4809275e-05
Overall Rank
8,137 | 44.18%
DOI
10.14778/3358701.3358703

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wei_vldb19,
        title = {{Discovery and Ranking of Embedded Uniqueness Constraints}},
        author = {Wei, Ziheng and Leck, Uwe and Link, Sebastian},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {13},
        pages = {2339--2352},
        doi = {10.14778/3358701.3358703},
        url = {https://doi.org/10.14778/3358701.3358703},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,690 Normalizing Property Graphs 2023 VLDB 6.1205402e-05
7,187 Discovery Algorithms for Embedded Functional Dependencies 2020 SIGMOD 5.6776392e-05
8,844 Hitting Set Enumeration with Partial Information for Unique Column Combination Discovery 2020 VLDB 5.3588479e-05
9,923 Efficient Differential Dependency Discovery 2024 VLDB 5.1955087e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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