Intelligent SPARQL endpoints: Optimizing execution performance by automatic query relaxation and queue scheduling

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dc.contributor.authorTorre-Bastida, Ana I.
dc.contributor.authorVillar-Rodriguez, Esther
dc.contributor.authorBilbao, Miren Nekane
dc.contributor.authorDel Ser, Javier
dc.contributor.editorCarretero, Jesus
dc.contributor.editorNakano, Koji
dc.contributor.editorKo, Ryan K.L.
dc.contributor.editorMueller, Peter
dc.contributor.editorGarcia-Blas, Javier
dc.contributor.institutionHPA
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:49:40Z
dc.date.available2024-07-24T11:49:40Z
dc.date.issued2016
dc.descriptionPublisher Copyright: © Springer International Publishing AG 2016.
dc.description.abstractThe Web of Data is widely considered as one of the major global repositories populated with countless interconnected and structured data prompting these linked datasets to be continuously and sharply increasing. In this context the so-called SPARQL Protocol and RDF Query Language is commonly used to retrieve and manage stored data by means of SPARQL endpoints, a query processing service especially designed to get access to these databases. Nevertheless, due to the large amount of data tackled by such endpoints and their structural complexity, these services usually suffer from severe performance issues, including inadmissible processing times. This work aims at overcoming this noted inefficiency by designing a distributed parallel system architecture that improves the performance of SPARQL endpoints by incorporating two functionalities: (1) a queuing system to avoid bottlenecks during the execution of SPARQL queries; and (2) an intelligent relaxation of the queries submitted to the endpoint at hand whenever the relaxation itself and the consequently lowered complexity of the query are beneficial for the overall performance of the system. To this end the system relies on a two-fold optimization criterion: the minimization of the query running time, as predicted by a supervised learning model; and the maximization of the quality of the results of the query as quantified by a measure of similarity. These two conflicting optimization criteria are efficiently balanced by two bi-objective heuristic algorithms sequentially executed over groups of SPARQL queries. The approach is validated on a prototype and several experiments that evince the applicability of the proposed scheme.en
dc.description.statusPeer reviewed
dc.format.extent15
dc.identifier.citationTorre-Bastida , A I , Villar-Rodriguez , E , Bilbao , M N & Del Ser , J 2016 , Intelligent SPARQL endpoints : Optimizing execution performance by automatic query relaxation and queue scheduling . in J Carretero , K Nakano , R K L Ko , P Mueller & J Garcia-Blas (eds) , Algorithms and Architectures for Parallel Processing - 16th International Conference, ICA3PP 2016, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 10048 LNCS , Springer Verlag , pp. 3-17 , 16th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2016 , Granada , Spain , 14/12/16 . https://doi.org/10.1007/978-3-319-49583-5_1
dc.identifier.citationconference
dc.identifier.doi10.1007/978-3-319-49583-5_1
dc.identifier.isbn9783319495828
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11556/1897
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85007179580&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.ispartofAlgorithms and Architectures for Parallel Processing - 16th International Conference, ICA3PP 2016, Proceedings
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsLinked open data
dc.subject.keywordsMultiobjective optimization
dc.subject.keywordsOntology management
dc.subject.keywordsQuery rewriting
dc.subject.keywordsSPARQL
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsGeneral Computer Science
dc.titleIntelligent SPARQL endpoints: Optimizing execution performance by automatic query relaxation and queue schedulingen
dc.typeconference output
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