Distributed Semantic Web Data Management in HBase and MySQL Cluster
Craig Franke, Samuel Morin, Artem Chebotko, John Abraham, Pearl, Brazier

TL;DR
This paper compares distributed RDF data management approaches using HBase and MySQL Cluster, demonstrating the potential of cloud computing for scalable Semantic Web data handling through empirical evaluation.
Contribution
It introduces distributed RDF storage and query schemes for HBase and MySQL Cluster and empirically compares their performance on real-world datasets.
Findings
Query evaluation reveals interesting patterns
Algorithms show promising results
Cloud computing has high potential for scalable Semantic Web data management
Abstract
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…
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