Evaluating spatial Skyline queries on changing data

Abstract

The amount of data being handled is enormous these days. To identify relevant data in large datasets, Skyline queries have been proposed. A traditional Skyline query selects those points that are the best ones based on user's preferences. Spatial Skyline Queries (SSQ) extend Skyline queries and allow the user to express preferences on the closeness between a set of data points and a set of query points. However, existing algorithms must be adapted to evaluate SSQ on changing data; changing data are data which regularly change over a period of time. In this work, we propose and empirically study three algorithms that use different techniques to evaluate SSQ on changing data. © 2013 Springer-Verlag.