Adapting Skyline Computation to the MapReduce Framework: Algorithms and Experiments

  • Boliang Zhang
  • Shuigeng Zhou
  • Jihong Guan
Conference paper

DOI: 10.1007/978-3-642-20244-5_39

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6637)
Cite this paper as:
Zhang B., Zhou S., Guan J. (2011) Adapting Skyline Computation to the MapReduce Framework: Algorithms and Experiments. In: Xu J., Yu G., Zhou S., Unland R. (eds) Database Systems for Adanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6637. Springer, Berlin, Heidelberg

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.

Keywords

Cloud computing MapReduce Skyline computation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Boliang Zhang
    • 1
    • 2
  • Shuigeng Zhou
    • 1
    • 2
  • Jihong Guan
    • 3
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Key Lab of Intelligent Information ProcessingFudan UniversityShanghaiChina
  3. 3.Dept. of Computer Science & TechnologyTongji UniversityShanghaiChina

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