CloudResearch(云服务)_21CloudResearch(云服务)_21.docVIP

Scalable Semantics – the Silver Lining of Cloud Com puting Andre Newman, Yuan-Fang Li and Jane Hunter School of ITEE, The University of Queensland 4072 Queensland, Australia {anewman,liyf,jane}@.au Abstract Semantic inferencing and querying across large-scale RDF triple stores is notoriously slow. Our objec-tive is to expedite this process by employing Google’s MapReduce framework to implement scale-out distri-buted querying and reasoning. This approach requires RDF graphs to be decomposed into smaller units that are distributed across computational nodes. RDF Mo-lecules appear to offer an ideal approach – providi ng an intermediate level of granularity between RDF graphs and triples. However, the original RDF mole-cule definition has inherent limitations that will ad-versely affect performance. In this paper, we propose a number of extensions to RDF molecules (hierarchy and ordering) to overcome these limitations. We then present some implementation details for our MapRe-duce-based RDF molecule store. Finally we evaluate the benefits of our approach in the context of the Bio-MANTA project – an application that requires integr a-tion and querying across large-scale protein-protein interaction datasets. 1. Introduction Semantic Web technologies such as RDF, OWL and SPARQL offer significant potential as technologies designed to support the integration of and reasoning across heterogeneous, disparate data sources. The widespread adoption of these technologies is being driven by the need to answer complex queries that de-mand the integration and processing of multiple re-lated, but disparate, multidisciplinary datasets. Datasets from disciplines including environmental sciences, biological sciences, social sciences, life sciences and health care sciences have been employing these tech-nologies to facilitate data correlation, integration and reasoning.  However, despite the widespread adoption of RDF, OWL and SPARQL within many disciplines