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Aggregation in Probabilistic Databases via Knowledge Compilation

Summary: Knowledge compilation enables aggregation over data: semiring/semimodule expressions compile into decomposition trees, yielding linear distribution. Tractable aggregates via decomposition trees; SPROUT prototype shows performance on TPC-H and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10495
Venue
VLDB
Year
2012
Pagerank
5.9820914e-05
Overall Rank
4,706 | 67.27%
DOI
-

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Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
31 Provenance Semirings 2007 PODS 0.0007857786
321 MCDB: A Monte Carlo Approach to Managing Uncertain Data 2008 SIGMOD 0.00027527389
1,106 Provenance for Aggregate Queries 2011 PODS 0.0001398766
1,238 Incremental Query Evaluation in a Ring of Databases 2010 PODS 0.00013114581
1,699 Sensitivity Analysis and Explanations for Robust Query Evaluation in Probabilistic Databases 2011 SIGMOD 0.00010858983
1,730 Conditioning Probabilistic Databases 2008 VLDB 0.00010736755
2,268 OLAP Over Uncertain and Imprecise Data 2005 VLDB 9.1497575e-05
6,079 Querying Uncertain Data with Aggregate Constraints 2011 SIGMOD 5.2223439e-05
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