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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
6063
Venue
SIGMOD
Year
2020
Pagerank
5.6776392e-05
Overall Rank
7,187 | 50.70%
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
5,690 Normalizing Property Graphs 2023 VLDB 6.1205402e-05
7,982 Fast Approximate Denial Constraint Discovery 2023 VLDB 5.5138609e-05
9,780 Discovering Functional Dependencies through Hitting Set Enumeration 2024 SIGMOD 5.2209769e-05
9,923 Efficient Differential Dependency Discovery 2024 VLDB 5.1955087e-05
10,848 Efficient Discovery of Relaxed Functional Dependencies 2025 VLDB 5.093636e-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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