A Hybrid Approach to Functional Dependency Discovery
Summary: HyFD, a hybrid FD discovery algorithm, combines fast approximation with efficient validation to uncover all minimal functional dependencies. It uses compact data structures to scale to 50+ attributes and millions of records, outperforming prior methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Thorsten Papenbrock (Hasso Plattner Institute)
- 2. Felix Naumann (Hasso Plattner Institute)
BibTeX Citation
@inproceedings{papenbrock_sigmod16,
title = {{A Hybrid Approach to Functional Dependency Discovery}},
author = {Papenbrock, Thorsten and Naumann, Felix},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2915203},
url = {https://dl.acm.org/doi/10.1145/2882903.2915203},
year = {2016}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 729 | Functional Dependency Discovery: An Experimental Evaluation of Seven Algorithms | 2015 | VLDB | 0.00014539421 |
| 1,216 | Partition Semantics for Relations | 1985 | PODS | 0.00011633715 |
| 1,374 | Data Profiling with Metanome | 2015 | VLDB | 0.00010986078 |
| 2,872 | Estimating the Confidence of Conditional Functional Dependencies | 2009 | SIGMOD | 8.0104819e-05 |
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