Efficient query evaluation on probabilistic databases

Efficient query evaluation on probabilistic databases We describe a framework for supporting arbitrarily complex SQL queries with “uncertain” predicates. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is query evaluation. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is # P -complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Efficient query evaluation on probabilistic databases

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Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-006-0004-3
Publisher site
See Article on Publisher Site

Abstract

We describe a framework for supporting arbitrarily complex SQL queries with “uncertain” predicates. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is query evaluation. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is # P -complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2007

References

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