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dc.contributor.authorDuch Brown, Amalia
dc.contributor.authorLugosi, Daniel
dc.contributor.authorPasarella Sánchez, Ana Edelmira
dc.contributor.authorZoltan Torres, Ana Cristina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2019-07-04T06:39:07Z
dc.date.available2019-07-04T06:39:07Z
dc.date.issued2019-06-07
dc.identifier.citationDuch, A. [et al.]. Dynamic pipelining of multidimensional range queries. "CEUR Workshop proceedings", 7 Juny 2019, vol. 2369, p. 1-5.
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/2117/165554
dc.description.abstractThe problem of evaluating orthogonal range queries efficiently has been studied widely in the data structures community. It has been common wisdom for several years that for queries containing more than 20% of the elements of the dataset a linear scanning of the data was the most efficient solution. In recent experimental works using modern hardware –with main memory and parallelism– the conclusion is that linear scan is preferable for almost every query configuration (even containing a 1% of the data). In this work we propose an alternative approach to evaluate multidimensional range queries based on the dynamic pipeline paradigm –using main memory and concurrency. Our aim is to prove that under this framework, it is possible to beat the performance of linear scanning by the one of hierarchical multidimensional data structures –such as kd trees, quad trees, Rtrees or similar.
dc.format.extent5 p.
dc.language.isoeng
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.lcshData structures (Computer science)
dc.subject.lcshDecision trees
dc.subject.otherMultidimensional range queries
dc.subject.otherParallelism
dc.subject.otherConcurrency
dc.subject.otherDynamic pipeline
dc.titleDynamic pipelining of multidimensional range queries
dc.typeArticle
dc.subject.lemacEstructures de dades (Informàtica)
dc.subject.lemacArbres de decisió
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://ceur-ws.org/Vol-2369/short14.pdf
dc.rights.accessOpen Access
drac.iddocument25403646
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/TIN2017-86727-C2-1-R
upcommons.citation.authorDuch, A.; Daniel Lugosi; Edelmira Pasarella; Zoltan, A.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameCEUR Workshop proceedings
upcommons.citation.volume2369
upcommons.citation.startingPage1
upcommons.citation.endingPage5


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution 3.0 Spain