Identifying interesting instances for probabilistic skylines

Abstract

Significant research efforts have recently been dedicated to modeling and querying uncertain data. In this paper, we focus on skyline analysis of uncertain data, modeled as uncertain objects with probability distributions over a set of possible values called instances. Computing the exact skyline probabilities of instances is expensive, and unnecessary when the user is only interested in instances with skyline probabilities over a certain threshold. We propose two filtering schemes for this case: a preliminary scheme that bounds an instance's skyline probability for filtering, and an elaborate scheme that uses an instance's bounds to filter other instances based on the dominance relationship. We experimentally demonstrate the effectiveness of our filtering schemes on both real and synthetic data sets and show the efficiency of our schemes compared with other algorithms. © 2010 Springer-Verlag.