The VLDB Journal

, Volume 22, Issue 6, pp 823–848 | Cite as

Anytime approximation in probabilistic databases

Regular Paper

Abstract

This article describes an approximation algorithm for computing the probability of propositional formulas over discrete random variables. It incrementally refines lower and upper bounds on the probability of the formulas until the desired absolute or relative error guarantee is reached. This algorithm is used by the SPROUT query engine to approximate the probabilities of results to relational algebra queries on expressive probabilistic databases.

Keywords

Probabilistic databases Query evaluation Anytime approximation Model-based approximation 

Supplementary material

778_2013_310_MOESM1_ESM.pdf (152 kb)
Supplementary material 1 (PDF 152 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of OxfordOxfordUK
  2. 2.Yale UniversityNew HavenUSA

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