Threshold-Based Direct Computation of Skyline Objects for Database with Uncertain Preferences

Abstract

Skyline queries aim at finding a set of skyline objects from the given database. For categorical data, the notion of preferences is used to determine skyline objects. There are many real world applications where the preference can be uncertain. In such contexts, it is relevant to determine the probability that an object is a skyline object in a database with uncertain pairwise preferences. Skyline query is to determine a set of objects having skyline probability greater than a threshold. In this paper, we address this problem. To the best of our knowledge, there has not been any technique which handles this problem directly. There have been proposals to compute skyline probability of individual objects but applying these for skyline query is computationally expensive. In this paper, we propose a holistic algorithm that determines the set of skyline objects for a given threshold and a database of uncertain preferences. We establish the relationship between skyline probability and the probability of the union of events. We guide our search to prune objects which are unlikely to be skyline objects. We report extensive experimental analysis to justify the efficiency of our algorithm.