Mining the Useful Skyline Set Based on the Acceptable Difference

Abstract

The efficiency of skyline query processing has recently received a lot of attention in database community. However, researchers often ignore that the skyline set will be beyond control in the applications which must deal with enormous data set. Consequently, it is not useful for users at all. In this paper, we propose a novel skyline reducing algorithm, i.e. SRANF. SRANF algorithm adopts the technique of noise filtering. It filters skyline noises directly on the original data set based on the acceptable difference, and returns the objects which can not be filtered from the original data set. Furthermore, our experiment demonstrated that SRANF is both efficient and effective.