An Algorithm to Update K-Dominant Skyline

Abstract

K-dominant skyline query technology reduces the result sets for a dataset with high dimensional spaces, which is more convenient for user to make decision. But all the existent algorithms are for static k, not suitable for dynamic k. To solve this problem, this paper proposed a new idea, updating the k-dominant skyline for new k by computing the partial points based on existent k-dominant skyline. Based on which, two algorithms are proposed for k increasing and decreasing. Furthermore, detailed theoretical analyses and extensive experiments demonstrate that the algorithms can effectively reduce redundant work, and keep the result correctly.