Anytime approximation in probabilistic databases

Authors:
Robert Fink;Jiewen Huang;Dan Olteanu
Affiliations:
University of Oxford, Oxford, UK;Yale University, New Haven, USA;University of Oxford, Oxford, UK
Venue:
The VLDB Journal — The International Journal on Very Large Data Bases
Year:
2013

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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.