Anytime approximation in probabilistic databases

Anytime approximation in probabilistic databases 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Anytime approximation in probabilistic databases

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-013-0310-5
Publisher site
See Article on Publisher Site

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.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2013

References

  • On the expressiveness of probabilistic XML models
    Abiteboul, S; Kimelfeld, B; Sagiv, Y; Senellart, P
  • Efficient query evaluation on probabilistic databases
    Dalvi, N; Suciu, D
  • A computing procedure for quantification theory
    Davis, M; Putnam, H

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