Extended k-dominant Skyline in High Dimensional Space

Abstract

Skyline queries have recently attracted a lot of attention for its intuitive query formulation. However, it retrieves too many objects, especially for high-dimensional data. To solve this problem, k-dominant skyline queries have been introduced recently, which can reduce the number of retrieved objects by relaxing the definition of dominance. However, sometimes, a k- dominant skyline query retrieves too few objects to analyze. In this paper, we extend the notion of k-domination by defining extended k-dominant skyline, which retrieves neither too many nor too few objects. We then develop a novel algorithm for extended k-dominant skyline computation. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithm on both real-life and synthetic datasets.