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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
11955
Venue
VLDB
Year
2019
Pagerank
4.5902231e-05
Overall Rank
8,085 | 43.76%
DOI
10.14778/3358701.3358703

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,910 Normalizing Property Graphs 2023 VLDB 5.2768691e-05
7,366 Discovery Algorithms for Embedded Functional Dependencies 2020 SIGMOD 4.7515248e-05
8,850 Hitting Set Enumeration with Partial Information for Unique Column Combination Discovery 2020 VLDB 4.4364648e-05
9,749 Efficient Differential Dependency Discovery 2024 VLDB 4.2897489e-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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