Adapting skyline computation to the MapReduce framework: algorithms and experiments

  • Authors:

  • Boliang Zhang;Shuigeng Zhou;Jihong Guan

  • Affiliations:

  • School of Computer Science, and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China;School of Computer Science, and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China;Dept. of Computer Science & Technology, Tongji University, Shanghai, China

  • Venue:
  • DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

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 result reduction. Based on different data partitioning strategies, three MapReduce style skyline computation algorithms are developed: MapReduce based BNL (MR-BNL), MapReduce based SFS (MR-SFS) and MapReduce based Bitmap (MR-Bitmap). Extensive experiments are conducted to evaluate and compare the three algorithms under different settings of data distribution, dimensionality, buffer size and cluster size.