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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
h0f4c18c932336d7e
Venue
SIGMOD
Year
2020
Pagerank
5.5658087e-05
Overall Rank
7,287 | 51.01%
DOI
10.1145/3318464.3389786

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wei_sigmod20,
        title = {{Discovery Algorithms for Embedded Functional Dependencies}},
        author = {Wei, Ziheng and Hartmann, Sven and Link, Sebastian},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3389786},
        url = {https://dl.acm.org/doi/10.1145/3318464.3389786},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
5,280 Fast Approximate Denial Constraint Discovery 2023 VLDB 6.1992888e-05
5,798 Normalizing Property Graphs 2023 VLDB 5.9905343e-05
9,958 Discovering Functional Dependencies through Hitting Set Enumeration 2024 SIGMOD 5.1038322e-05
10,106 Efficient Differential Dependency Discovery 2024 VLDB 5.0789354e-05
11,254 Efficient Discovery of Relaxed Functional Dependencies 2025 VLDB 4.9793485e-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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