NDLI: Comparison of the Efficiency of MapReduce and Bulk Synchronous Parallel Approaches to Large Network Processing

Content Provider	IEEE Xplore Digital Library
Author	Kajdanowicz, T. Indyk, W. Kazienko, P. Kukul, J.
Copyright Year	2012
Abstract	Network structures, especially social networks, grow rapidly and provide huge datasets intractable to analyse. In this paper, two parallel approaches to process large graph structures within the Hadoop environment were compared: Bulk Synchronous Parallel (BSP) and MapReduce (MR). The experimental studies were carried out for two different graph problems: collective classification by means of Relational Influence Propagation (RIP) and Single Source Shortest Path (SSSP) calculation. The appropriate BSP and MapReduce algorithms for these problems were applied to various network datasets differing in size and structural profile, originating from three domains: telecommunication, multimedia and microblog. The collected results revealed that iterative graph processing with BSP implementation significantly outperform popular MapReduce, especially for algorithms with many iterations and sparse communication. However, MapReduce still remains the only alternative for enormous networks.
Starting Page	218
Ending Page	225
File Size	499565
Page Count	8
File Format	PDF
ISBN	9781467351645
e-ISBN	9781467351645
DOI	10.1109/ICDMW.2012.135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-10
Publisher Place	Belgium
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Collective Classification Networked Data Bulk Synchronous Parallel Cloud Computing Big Data Parallel Processing Large Graph Processing MapReduce Shortest Path
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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