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 ...

• Type: Conference
• Year: 2009
• Where: DASFAA
• Authors: Thomas Bernecker, Hans-Peter Kriegel, Matthias Renz, Andreas Züfle
• Keywords: DASFAA 2009, Database, Ranking Algorithm, Similarity Ranking Algorithm, Uncertain Data