A two-phase data space partitioning for efficient skyline computation

The skyline has attracted a lot of attention due to its wide application in various fields. However, the skyline computation is a challenging issue as there is a high probability that today's applications deal with large and high-dimensional data. As skyline computation for such huge amount of data consumes much time, parallel and distributed skyline computations are considered. State-of-the-art methods for parallel and distributed skyline computations use various data space partitioning techniques. However, these methods are not efficient, as in certain cases, these methods perform unnecessary skyline computations in a partitioned space, where local-skyline tuples do not contribute to the global-skyline. This may impose additional processing overload and enlarge the overall skyline computation time. In this paper, we propose a novel data space partitioning method for parallel and distributed skyline computation that consists of two-phases: diagonal and entropy score curve based partitioning. The proposed method produces a small set of local-skyline tuples and leads to a more sophisticated merging step. The experiment results demonstrate that the proposed method reduces the number of comparisons and processing time of skyline computation in large amount of data when compared with the existing state-of-the-art methods.