JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (5): 22-28.doi: 10.6040/j.issn.1672-3961.0.2014.367

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Parallel implementing probabilistic spreading algorithm using MapReduce programming mode

HE Dongzhi, ZHANG Jifeng, ZHAO Pengfei   

  1. School of Software Engineering, Beijing University of Technology, Beijing 100124, China
  • Received:2014-12-16 Revised:2015-06-10 Online:2018-10-20 Published:2014-12-16

Abstract: In order to overcome the limitations of the serial probabilistic spreading algorithm in dealing with large-scale dataset, a parallelization of the algorithm was put forth by using MapReduce. The complex computing tasks were decomposed into a series of MapReduce job flow for distributed parallel processing on Hadoop. The input and output data of every step were stored in the Hadoop distributed file system. Hit ratio was used to compare the parallelizable probabilistic spreading algorithm versus the global ranking method performance. Speedups of the parallelizable algorithm were compared while the amount of data and the number of nodes was different. Experiment results showed that the probabilistic spreading algorithm based on MapReduce had good parallelism and had higher hit ratio than the global ranking method. Data scale that can be handled by the serial algorithm was expanded, and the operation speed of the algorithm was raised.

Key words: cloud computing paltform, probabilistic spreading algorithm, MapReduce, distributed, bipartite network

CLC Number: 

  • TP311
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