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dc.contributor.authorSaleem, Muhammad
dc.contributor.authorHasnain, Ali
dc.contributor.authorNgonga Ngomo, Axel-Cyrille
dc.date.accessioned2019-01-29T09:24:15Z
dc.date.issued2018-01-12
dc.identifier.citationSaleem, Muhammad, Hasnain, Ali, & Ngonga Ngomo, Axel-Cyrille. (2018). LargeRDFBench: A billion triples benchmark for SPARQL endpoint federation. Journal of Web Semantics, 48, 85-125. doi: https://doi.org/10.1016/j.websem.2017.12.005en_IE
dc.identifier.issn1570-8268
dc.identifier.urihttp://hdl.handle.net/10379/14875
dc.description.abstractGathering information from the distributed Web of Data is commonly carried out by using SPARQL query federation approaches. However, the fitness of current SPARQL query federation approaches for real applications is difficult to evaluate with current benchmarks as they are either synthetic, too small in size and complexity or do not provide means for a fine-grained evaluation. We propose LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases. We evaluate state-of-the-art SPARQL endpoint federation approaches on this benchmark with respect to their query runtime, triple pattern-wise source selection, number of endpoints requests, and result completeness and correctness. Our evaluation results suggest that the performance of current SPARQL query federation systems on simple queries (in terms of total triple patterns, query result set sizes, execution time, use of SPARQL features etc.) does not reflect the systems performance on more complex queries. Moreover, current federation systems seem unable to deal with real queries that involve processing large intermediate result sets or lead to large result sets.en_IE
dc.description.sponsorshipThis work was supported by the BMWi project SAKE. We are especially thankful to Helena Deus (Foundations Medicine, Cambridge, MA, USA) and Shanmukha Sampath (Democritus University of Thrace, Alexandroupoli, Greece) for providing real use case large data queries.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherElsevieren_IE
dc.relation.ispartofJournal Of Web Semanticsen
dc.subjectBenchmarken_IE
dc.subjectSPARQLen_IE
dc.subjectFederated queriesen_IE
dc.subjectLinked Dataen_IE
dc.subjectRDFen_IE
dc.titleLargeRDFBench: A billion triples benchmark for SPARQL endpoint federationen_IE
dc.typeArticleen_IE
dc.date.updated2019-01-23T16:21:12Z
dc.identifier.doi10.1016/j.websem.2017.12.005
dc.local.publishedsourcehttps://doi.org/10.1016/j.websem.2017.12.005en_IE
dc.description.peer-reviewedpeer-reviewed
dc.description.embargo2020-01-12
dc.internal.rssid15742088
dc.local.contactSyed Muhammad Ali Hasnain, Deri, Ida Business Park, Lower Dangan, Galway. Email: ali.hasnain@nuigalway.ie
dc.local.copyrightcheckedYes
dc.local.versionPUBLISHED
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