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Information Dependencies

Summary: Introduce an entropy-based information dependency (InD) measure quantifying residual uncertainty of Y given X in a relation and derive universal arithmetic InD inequalities that hold for any instance. Show FDs/MVDs arise as zero constraints (Armstrong’s axioms follow) and prove any constraint set consistent with the inequalities is approximately realizable, enabling principled numeric constraints and new data‑mining applications. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1218
Venue
PODS
Year
2000
Pagerank
7.3316755e-05
Overall Rank
3,240 | 77.47%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
472 Bottom-Up Computation of Sparse and Iceberg CUBEs 1999 SIGMOD 0.00022346384
523 Recovering Information from Summary Data 1997 VLDB 0.00021089782
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