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Quantifying the Loss of Acyclic Join Dependencies

Summary: Show KL-divergence between the universal relation and the acyclic join result exactly captures AJD loss, linking information-theoretic loss to the classic redundant-tuple measure. Prove a deterministic lower bound on redundant-tuple percentage and a high-probability upper bound under a random-database model, with both bounds coinciding asymptotically for large databases. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1877
Venue
PODS
Year
2023
Pagerank
4.3341665e-05
Overall Rank
9,486 | 34.01%
DOI
10.1145/3584372.3588658

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Rank Citing Paper Year Venue Pagerank
10,359 Smallest Synthetic Witnesses for Conjunctive Queries 2025 PODS 4.1945683e-05
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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
834 Learning Linear Regression Models over Factorized Joins 2016 SIGMOD 0.00016135159
1,884 Normal forms and relational database operators 1979 SIGMOD 0.00010215563
3,277 A Layered Aggregate Engine for Analytics Workloads 2019 SIGMOD 7.2871625e-05
7,076 Mining Approximate Acyclic Schemes from Relations 2020 SIGMOD 4.8426354e-05
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