Earticle

초록

영어

The MapReduce framework has been widely used to process and analyze largescale datasets over large clusters. As an essential problem, join operation among large clusters attracts more and more attention in recent years due to the utilization of MapReduce. Many strategies have been proposed to improve the efficiency of distributed join, among which bloomfilter is a successful one. However, the bloomfilter’s potential has not yet been fully exploited, especially in the MapReduce environment. In this paper, three strategies are presented to build the bloomfilter for the large datasets using MapReduce. Based on these strategies, we design two algorithms for two-way join and one algorithm for multi-way join. The experimental results show that our algorithms can significantly improve the efficiency of current join algorithm. Moreover, cost models of these algorithms are characterized in order to find out the way of improving the performance of two-way and multi-way joins.

목차

Abstract
 1 Introduction
 2 MapReduce and Bloomlter
  2.1 MapReduce
  2.2 Bloomlter
 3 Computing Bloomlters using MapReduce
 4 Bloomjoins using MapReduce
  4.1 Two-way Joins using MapReduce
  4.2 Multi-way Joins using MapReduce
 5 Cost Model of Bloomjoins using MapReduce
  5.1 Cost Model of Computing Bloomlters using MapReduce
  5.2 Cost Model of Two-way Bloomjoins
  5.3 Cost Model of Multi-way Joins using MapReduce
  5.4 Cost Model Validation
 6 Related Work
 7 Conclusion and Future Work
 8 Acknowledgements
 References

키워드

저자

• Changchun Zhang [ School of Computer Science and Technology, University of Science and Technology of China ]
• Lei Wu [ School of Computer Science and Technology, University of Science and Technology of China ]
• Jing Li [ School of Computer Science and Technology, University of Science and Technology of China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605