Skyline Computation under MapReduce Framework
Zhang Boliang · Jisuanji kexue yu tansuo · 2011
Skyline computation,due to its wide applications in multi-objective decision making and data visualization,has attracted many research interests in database community recently.Aiming at cloud computing applications,this paper addresses the problem of Skyline computation under the MapReduce framework.As a parallel programming model for data-intensive computing applications,MapReduce runs on a cluster of commercial PCs with the main idea of task decomposition and solution reduction.Based on different data division strategies,this paper proposes three algorithms:MapReduce based block-nested-loops(MR-BNL),MapReduce based sort-filter-skyline(MR-SFS) and MapReduce based bitmap(MR-Bitmap).It conducts extensive experiments to evaluate and compare the three algorithms under different situations of different data distributions,dimensions and buffer sizes.