Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120110
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dc.contributor.authorZhang, W.en
dc.contributor.authorSheng, Q.en
dc.contributor.authorQin, Y.en
dc.contributor.authorTaylor, K.en
dc.contributor.authorYao, L.en
dc.date.issued2018en
dc.identifier.citationWorld Wide Web, 2018; 21(4):1015-1035en
dc.identifier.issn1386-145Xen
dc.identifier.issn1573-1413en
dc.identifier.urihttp://hdl.handle.net/2440/120110-
dc.description.abstractOne of the challenges of managing an RDF database is predicting performance of SPARQL queries before they are executed. Performance characteristics, such as the execution time and memory usage, can help data consumers identify unexpected long-running queries before they start and estimate the system workload for query scheduling. Extensive works address such performance prediction problem in traditional SQL queries but they are not directly applicable to SPARQL queries. In this paper, we adopt machine learning techniques to predict the performance of SPARQL queries. Our work focuses on modeling features of a SPARQL query to a vector representation. Our feature modeling method does not depend on the knowledge of underlying systems and the structure of the underlying data, but only on the nature of SPARQL queries. Then we use these features to train prediction models. We propose a two-step prediction process and consider performances in both cold and warm stages. Evaluations are performed on real world SPRAQL queries, whose execution time ranges from milliseconds to hours. The results demonstrate that the proposed approach can effectively predict SPARQL query performance and outperforms state-of-the-art approaches.en
dc.description.statementofresponsibilityWei Emma Zhang, Quan Z. Sheng, Yongrui Qin, Kerry Taylor, Lina Yaoen
dc.language.isoenen
dc.publisherSpringer Natureen
dc.rights© Springer Science+Business Media, LLC 2017en
dc.subjectSPARQL; feature modelling; prediction; query performanceen
dc.titleLearning-based SPARQL query performance modeling and predictionen
dc.typeJournal articleen
dc.identifier.rmid0030119269en
dc.identifier.doi10.1007/s11280-017-0498-1en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140100104en
dc.identifier.pubid478532-
pubs.library.collectionComputer Science publicationsen
pubs.library.teamDS14en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidZhang, W. [0000-0002-0406-5974]en
Appears in Collections:Computer Science publications

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