Abstract
Skyline operators are fascinating concepts that let the users extend and evolve a database system. SKY-MR+ algorithm is an efficient framework implemented for skyline operators and queries which uses quad-tree-based histogram, but faces serious limitations and provides inconsistent execution time especially for High datasets. In such cases, it also reports higher processing time with the increase of number of machines in the system. In this paper, an effective framework for skyline queries using principal component analysis (EFSQ-PCA) is proposed and developed which reduces the execution time for High datasets even in the cases of increase in number of machines in the system. The proposed mechanism finds the “Points in Region” using principal component analysis and this forms the base to increase the processing capabilities of skyline queries on various synthetic datasets. Experimental results show improvement in execution time of the proposed EFSQ-PCA as compared to current state of the art under different numbers of dimensions for dataset.
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