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Issue No. 03 - May-June (2013 vol. 15)
ISSN: 1520-9202
pp: 36-43
John Abraham , University of Texas–Pan American
Pearl Brazier , University of Texas–Pan American
Samuel Morin , University of Texas–Pan American
Artem Chebotko , University of Texas–Pan American
Craig Franke , 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.
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
John Abraham, Pearl Brazier, Samuel Morin, Artem Chebotko, Craig Franke, "Efficient Processing of Semantic Web Queries in HBase and MySQL Cluster", IT Professional, vol. 15, no. , pp. 36-43, May-June 2013, doi:10.1109/MITP.2012.42
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