Probabilistic Ranking in Uncertain Vector Spaces

12 years 8 months ago
Probabilistic Ranking in Uncertain Vector Spaces
Abstract. In many application domains, e.g. sensor databases, traffic management or recognition systems, objects have to be compared based on positionally and existentially uncertain data. Feature databases with uncertain data require special methods for effective similarity search. In this paper, we propose a probabilistic similarity ranking algorithm which computes the results dynamically based on the complete information given by inexact object representations. Hence, this can be performed in an effective and efficient way. We assume that the objects are given by a set of points in a vector space with confidence values following the discrete uncertainty model. Based on this representation, we introduce a probabilistic ranking algorithm that is able to reduce significantly the computational complexity of the computation of the probability that an object is at a certain ranking position. In a detailed experimental evaluation, we demonstrate the benefits of this approach compared ...
Thomas Bernecker, Hans-Peter Kriegel, Matthias Ren
Added 19 May 2010
Updated 19 May 2010
Type Conference
Year 2009
Authors Thomas Bernecker, Hans-Peter Kriegel, Matthias Renz, Andreas Züfle
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