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CORR
2011
Springer

A Novel Probabilistic Pruning Approach to Speed Up Similarity Queries in Uncertain Databases

8 years 15 days ago
A Novel Probabilistic Pruning Approach to Speed Up Similarity Queries in Uncertain Databases
Abstract— In this paper, we propose a novel, effective and efficient probabilistic pruning criterion for probabilistic similarity queries on uncertain data. Our approach supports a general uncertainty model using continuous probabilistic density functions to describe the (possibly correlated) uncertain attributes of objects. In a nutshell, the problem to be solved is to compute the PDF of the random variable denoted by the probabilistic domination count: Given an uncertain database object B, an uncertain reference object R and a set D of uncertain database objects in a multi-dimensional space, the probabilistic domination count denotes the number of uncertain objects in D that are closer to R than B. This domination count can be used to answer a wide range of probabilistic similarity queries. Specifically, we propose a novel geometric pruning filter and introduce an iterative filter-refinement strategy for conservatively and progressively estimating the probabilistic domination ...
Thomas Bernecker, Tobias Emrich, Hans-Peter Kriege
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2011
Where CORR
Authors Thomas Bernecker, Tobias Emrich, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, Andreas Züfle
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