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Improving Join Performance for SPARQL Query Processing in the Clouds
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 6,  2016, pp.700-709
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.6.700
 Title & Authors
Improving Join Performance for SPARQL Query Processing in the Clouds
Choi, Gyu-Jin; Son, Yun-Hee; Lee, Kyu-Chul;
 
 Abstract
Recently, with the rapid growth of LOD (Linked Open Data) existing methods based on a single machine have limitation in performance. Existing solutions use distributed framework such as Mapreduce in order to improve the performance. However, the MapReduce framework for processing SPARQL queries involves multiple MapReduce jobs and additional costs incurred. In addition, the problem of unnecessary data processing arises. In this study, we proposed a method to reduce the number of MapReduce jobs during SPARQL query processing and join indexes based on Bitmap for minimizing the costs of processing unnecessary data.
 Keywords
semantic web;MapReduce;SPARQL;RDF;join index;bitmap index;
 Language
Korean
 Cited by
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