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Efficient Processing of Semantic Web Queries in HBase and MySQL Cluster
May-June 2013 (vol. 15 no. 3)
pp. 36-43
Craig Franke, University of Texas–Pan American
Samuel Morin, University of Texas–Pan American
Artem Chebotko, University of Texas–Pan American
John Abraham, University of Texas–Pan American
Pearl Brazier, University of Texas–Pan American
With rapid growth of the Semantic Web, the authors' research focuses on designing a scalable resource description framework (RDF) database system that can efficiently process SPARQL queries over large RDF datasets. In this article, they present their design, architecture, storage schemes, and performance evaluation of two efficient RDF data management approaches that use state-of-the-art cloud and relational database technologies-HBase and MySQL Cluster.
Index Terms:
Resource description framework,Databases,Semantic Web,Middleware,Clustering algorithms,Distributed processing,Information technology,Performance evaluation,Query processing,information technology,cloud computing,distributed database,Semantic Web,RDF,SPARQL,SQL,HBase,MySQL Cluster,query; performance,scalability
Craig Franke, Samuel Morin, Artem Chebotko, John Abraham, Pearl Brazier, "Efficient Processing of Semantic Web Queries in HBase and MySQL Cluster," IT Professional, vol. 15, no. 3, pp. 36-43, May-June 2013, doi:10.1109/MITP.2012.42
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