Threshold-based probabilistic top- k dominating queries

Threshold-based probabilistic top- k dominating queries Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Top- k dominating queries are very important in many applications including decision making in a multidimensional space. In this paper, we study the problem of efficiently computing top- k dominating queries on uncertain data. We first formally define the problem. Then, we develop an efficient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deficiency in an exact computation, we develop an efficient randomized algorithm with an accuracy guarantee. Our extensive experiments demonstrate that both algorithms are quite efficient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Threshold-based probabilistic top- k dominating queries

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Publisher
Springer-Verlag
Copyright
Copyright © 2010 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-009-0162-1
Publisher site
See Article on Publisher Site

Abstract

Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Top- k dominating queries are very important in many applications including decision making in a multidimensional space. In this paper, we study the problem of efficiently computing top- k dominating queries on uncertain data. We first formally define the problem. Then, we develop an efficient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deficiency in an exact computation, we develop an efficient randomized algorithm with an accuracy guarantee. Our extensive experiments demonstrate that both algorithms are quite efficient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice.

Journal

The VLDB JournalSpringer Journals

Published: Apr 1, 2010

References

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