An Efficient Processing of k-Dominant Skyline Query in MapReduce

Abstract

Filtering uninteresting data is important to utilize "big data". Skyline query is one of popular techniques to filter uninteresting data, in which it selects a set of points that are not dominated by another from a given large database. However, a skyline query often retrieves too many points to analyze intensively especially for high-dimensional dataset. In order to solve the problem, k-dominant skyline queries have been introduced, which can control the number of retrieved points. However, conventional algorithms for computing k-dominant skyline queries are not well suited for parallel and distributed environments, such as the MapReduce framework. In this paper we considered an efficient parallel algorithm to process k-dominant skyline query in the MapReduce framework. Extensive experiments are conducted to evaluate the algorithm under different settings of data distribution, dimensionality, and cardinality.