Scalable reduction of large datasets to interesting subsets

De Montfort University Open Research Archive

Show simple item record Williams, G. T. en Weaver, J. en Atre, M. en Hendler, James en 2012-08-14T09:05:04Z 2012-08-14T09:05:04Z 2010-11
dc.identifier.citation Williams, G.T., Weaver, J., Atre, M. and Hendler, J.A. (2010) Scalable reduction of large datasets to interesting subsets. Web Semantics: Science, Services and Agents on the World Wide Web, 8 (4), pp. 365-373 en
dc.identifier.issn 1570-8268
dc.description.abstract With a huge amount of RDF data available on the web, the ability to find and access relevant information is crucial. Traditional approaches to storing, querying, and reasoning fall short when faced with web-scale data. We present a system that combines the computational power of large clusters for enabling large-scale reasoning and data access with an efficient data structure for storing and querying the accessed data on a traditional personal computer or other resource-constrained device. We present results of using this system to load the 2009 Billion Triples Challenge dataset, materialize RDFS inferences, extract an “interesting” subset of the data using a large cluster, and further analyze the extracted data using a personal computer, all in the order of tens of minutes. en
dc.language.iso en en
dc.publisher Elsevier en
dc.subject Billion Triples Challenge en
dc.subject scalability en
dc.subject Parallel en
dc.subject inferencing en
dc.subject query en
dc.subject Triplestore en
dc.title Scalable reduction of large datasets to interesting subsets en
dc.type Article en
dc.researchgroup Software Technology Research Laboratory (STRL) en
dc.peerreviewed Yes en

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