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 ...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Thomas Bernecker, Tobias Emrich, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, Andreas Züfle
• Keywords: CORR 2011, Education, Probabilistic Domination Count, Probabilistic Similarity, Uncertain Databases