On making skyline queries resistant to outliers

Abstract

This paper deals with the issue of retrieving the most preferred objects (in the sense of Skyline queries, i.e., of Pareto ordering) from a collection involving outliers. Indeed, many real-world datasets, for instance from ad sales websites, contain odd data and it is important to limit the impact of such odd data (outliers) on the result of skyline queries, and prevent them from hiding more interesting points. The approach we propose relies on the notion of fuzzy typicality and makes it possible to compute a graded skyline where each answer is associated with both a degree of membership to the skyline and a typicality degree. A GPU-based parallel implementation of the algorithm is described and experimental results are presented, which show the scalability of the approach.