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Distributed Graph Mining: A study of performance advantages in distributed data mining paradigms when processing graphs using PageRank on a single node cluster
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Distributed data mining is a relatively new area within computer science that is steadily growing, emerging from the demands of being able to gather and process various distributed data by utilising clusters. This report presents the properties of graph structured data and what paradigms to use for efficiently processing the data type, based on comprehensive theoretical studies applied on practical tests performed on a single node cluster. The results in the study showcase the various performance aspects of processing graph data, using different open source paradigm frameworks and amount of shards used on input. A conclusion to be drawn from this study is that there are no real performance advantages to using distributed data mining paradigms specifically developed for graph data on single machines. 

Place, publisher, year, edition, pages
2015.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-166449OAI: oai:DiVA.org:kth-166449DiVA: diva2:811098
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Examiners
Available from: 2015-05-12 Created: 2015-05-10 Last updated: 2015-05-12Bibliographically approved

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fulltext(5887 kB)1552 downloads
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83007ef3cca55d30e8b22ce7df32fd7f706a635645e3d1fc7021ddbb0e0340e60795de8850cbdc1c972f9a4b385f6e362c5af429e32fe7c491ec2ac06ce06606
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf