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Mining Approximate Acyclic Schemes from Relations

Summary: Maimon mines approximate MVDs to derive acyclic schemes from data. It uses information-theoretic approximation to jointly mine approximate MVDs and reconstruct acyclic schemas, scalable to 1M rows and 30 columns, and robust to noise. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5848
Venue
SIGMOD
Year
2020
Pagerank
5.7369354e-05
Overall Rank
6,929 | 52.47%
DOI
10.1145/3318464.3380573

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kenig_sigmod20,
        title = {{Mining Approximate Acyclic Schemes from Relations}},
        author = {Kenig, Batya and Mundra, Pranay and Prasaad, Guna and Salimi, Babak and Suciu, Dan},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3380573},
        url = {https://dl.acm.org/doi/10.1145/3318464.3380573},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
3,441 A Statistical Perspective on Discovering Functional Dependencies in Noisy Data 2020 SIGMOD 7.4138323e-05
9,700 Quantifying the Loss of Acyclic Join Dependencies 2023 PODS 5.2351259e-05
10,652 Smallest Synthetic Witnesses for Conjunctive Queries 2025 PODS 5.093636e-05
11,564 Statistical Schema Learning using Occam's Razor 2022 SIGMOD 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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