Various computing and data resources on the Web are being enhanced with
machine-interpretable semantic descriptions to facilitate better search,
discovery and integration. This interconnected metadata constitutes the
Semantic Web, whose volume can potentially grow the scale of the Web. Efficient
management of Semantic Web data, expressed using the W3C's Resource Description
Framework (RDF), is crucial for supporting new data-intensive,
semantics-enabled applications. In this work, we study and compare two
approaches to distributed RDF data management based on emerging cloud computing
technologies and traditional relational database clustering technologies. In
particular, we design distributed RDF data storage and querying schemes for
HBase and MySQL Cluster and conduct an empirical comparison of these approaches
on a cluster of commodity machines using datasets and queries from the Third
Provenance Challenge and Lehigh University Benchmark. Our study reveals
interesting patterns in query evaluation, shows that our algorithms are
promising, and suggests that cloud computing has a great potential for scalable
Semantic Web data management.Comment: In Proc. of the 4th IEEE International Conference on Cloud Computing
(CLOUD'11
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