Distributed Semantic Web Data Management in HBase and MySQL Cluster

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 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.