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Discovery Algorithms for Embedded Functional Dependencies

Summary: eFD discovery is NP-complete and W[2]-hard in the output, with a larger minimum solution space than classical FDs. Row-, column-, and hybrid data structures with efficient search enable scalable eFD discovery; ranking by redundant values highlights meaningful eFDs and showcases completeness benefits for approximate and genuine FDs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6001
Venue
SIGMOD
Year
2020
Pagerank
4.7515248e-05
Overall Rank
7,366 | 48.76%
DOI
10.1145/3318464.3389786

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
5,910 Normalizing Property Graphs 2023 VLDB 5.2768691e-05
8,836 Fast Approximate Denial Constraint Discovery 2023 VLDB 4.4393184e-05
9,646 Discovering Functional Dependencies through Hitting Set Enumeration 2024 SIGMOD 4.3109001e-05
9,749 Efficient Differential Dependency Discovery 2024 VLDB 4.2897489e-05
10,587 Efficient Discovery of Relaxed Functional Dependencies 2025 VLDB 4.1945683e-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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