VMPSP: Efficient Skyline Computation Using VMP-Based Space Partitioning

Abstract

The skyline query returns a set of interesting points that are not dominated by any other points in the multi-dimensional data sets. This query has already been considerably studied over last several years in preference analysis and multi-criteria decision making applications fields. Space partitioning, the best non-index framework, has been proposed and existing methods based on it do not consider the balance of partitioned subspaces. To overcome this limitation, we first develop a cost evaluation model of space partitioning in skyline computation, propose an efficient approach to compute the skyline set using balanced partitioning. We illustrate the importance of the balance in partitioning. Based on this, we propose a method to construct a balanced partitioning point VMP whose ith attribute value is the median value of all points in ith dimension. We also design a structure RST to reduce dominance tests among those subspaces which are comparable. The experimental evaluation indicates that our algorithm is faster at least several times than existing state-of-the-art algorithms.