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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
5786
Venue
SIGMOD
Year
2020
Pagerank
4.8426354e-05
Overall Rank
7,076 | 50.78%
DOI
10.1145/3318464.3380573

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
4,127 A Statistical Perspective on Discovering Functional Dependencies in Noisy Data 2020 SIGMOD 6.4310458e-05
9,486 Quantifying the Loss of Acyclic Join Dependencies 2023 PODS 4.3341665e-05
10,359 Smallest Synthetic Witnesses for Conjunctive Queries 2025 PODS 4.1945683e-05
11,366 Statistical Schema Learning using Occam's Razor 2022 SIGMOD 4.1945683e-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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