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dc.contributor.authorVillar-Rodriguez, E.
dc.contributor.authorBilbao, M.N.
dc.contributor.authorDel Ser, J. 
dc.contributor.authorTorre-Bastida, A.I.
dc.date.accessioned2020-10-19T15:31:38Z
dc.date.available2020-10-19T15:31:38Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1180
dc.description.abstractThe Web of Data is widely considered as one of the major global repositories populated with countless interconnected and struc- tured 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 complex- ity, 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 exe- cution 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 over- all 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 similar- ity. 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_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectSPARQLen_US
dc.subjectquery rewritingen_US
dc.subjectLinked Open Dataen_US
dc.subjectontology manage- menten_US
dc.subjectmultiobjective optimizationen_US
dc.titleIntelligent SPARQL Endpoints: Optimizing Execution Performance by Automatic Query Relaxation and Queue Schedulingen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doihttps://doi.org/10.1007/978-3-319-49583-5_1
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-319-49583-5_1en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleSpringer International Publishingen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España