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Computing a Similarity Coefficient for Mining Massive Data Sets

  • M. CoşulschiEmail author
  • M. Gabroveanu
  • A. Sbîrcea
Chapter
  • 636 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 627)

Abstract

Large amounts of data can be found today in all areas as a result of various processes like e-commerce transactions, banking or credit card transactions, or web navigation user sessions (recorded into web server logs). The development and implementation of algorithms able to process huge amounts of data have become more affordable due to cloud computing and the MapReduce programming model, which, in turn, enabled the development of some open-source frameworks, such as Apache Hadoop. Based on the values obtained by computing the Jaccard similarity coefficients for two very large graphs, we have analysed in this paper the connections and influences that certain nodes have over other nodes. Also, we have illustrated how the Apache Hadoop framework and the MapReduce programming model can be used for a large amount of computations.

Keywords

Big data Virtualization Hadoop Mapreduce Jaccard similarity 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CraiovaCraiovaRomania

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