Job-optimized Map-side join processing using MapReduce and HBase with abstract RDF data

Hyunsuk Oh, Sejin Chun, Sungkwang Eom, Kyong Ho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The amount of RDF data being published on the Web is increasing at a massive rate. MapReduce-based distributed frameworks have become the general trend in processing SPARQL queries against the RDF data. Currently, query processing systems that use MapReduce have not been able to keep up with increases in semantic annotated data, resulting in non-interactive SPARQL query processing. The principal reason is that intermediate query results from join operations in a MapReduce framework are so massive that network bandwidth and hard disk drive I/O speeds may not keep pace with the processing speed. In this paper, we present an efficient SPARQL processing system that uses MapReduce and HBase. The system runs a job optimized query plan using our proposed abstract RDF data to decrease the amount of intermediate data, thus resulting in faster query processing performance. We also present an efficient algorithm of using Map-side joins while also using the abstract RDF data to filter out unneeded RDF data. Experimental results show that the proposed approach demonstrates better performance when processing queries with a large set of inputs than those found in previous works.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages425-432
Number of pages8
ISBN (Electronic)9781467396172
DOIs
Publication statusPublished - 2016 Feb 2
Event2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015 - Singapore, Singapore
Duration: 2015 Dec 62015 Dec 9

Publication series

NameProceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015
Volume1

Other

Other2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015
CountrySingapore
CitySingapore
Period15/12/615/12/9

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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    Oh, H., Chun, S., Eom, S., & Lee, K. H. (2016). Job-optimized Map-side join processing using MapReduce and HBase with abstract RDF data. In Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015 (pp. 425-432). [981706] (Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WI-IAT.2015.122