DBScholar

Back to papers

A Statistical Perspective on Discovering Functional Dependencies in Noisy Data

Summary: FD discovery under noise as structure learning on binary variables; variables are data functions. FDX: sparse-regression framework turning FD discovery into regression; robust to noise/missing data, scalable to large datasets with ~2x F1 gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6026
Venue
SIGMOD
Year
2020
Pagerank
7.4138323e-05
Overall Rank
3,441 | 76.40%
DOI
10.1145/3318464.3389749

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod20,
        title = {{A Statistical Perspective on Discovering Functional Dependencies in Noisy Data}},
        author = {Zhang, Yunjia and Guo, Zhihan and Rekatsinas, Theodoros},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3389749},
        url = {https://dl.acm.org/doi/10.1145/3318464.3389749},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 15 of 15 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers