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dc.contributor.authorBarba-González, Cristóbal
dc.contributor.authorGarcía-Nieto, José
dc.contributor.authorAldana-Montes, Jose Francisco 
dc.contributor.authorNebro, Antonio J.
dc.date.accessioned2017-04-20T08:52:20Z
dc.date.available2017-04-20T08:52:20Z
dc.date.created2017
dc.date.issued2017-04-20
dc.identifier.urihttp://hdl.handle.net/10630/13476
dc.description.abstractBig Data Optimization is the term used to refer to optimization problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster computing system for solving multi-objective Big Data Optimization problems. Our purpose is to study the influence of accessing data stored in the Hadoop File System (HDFS) in each evaluation step of a metaheuristic and to provide a software tool to solve these kinds of problems. This tool combines the jMetal multi-objective optimization framework with Apache Spark. We have carried out experiments to measure the performance of the proposed parallel infrastructure in an environment based on virtual machines in a local cluster comprising up to 100 cores. We obtained interesting results for computational e ort and propose guidelines to face multi-objective Big Data Optimization problems.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMinería de datoses_ES
dc.subject.othermulti-objective optimizationes_ES
dc.subject.otherBig Dataes_ES
dc.subject.otherjMetales_ES
dc.subject.otherSparkes_ES
dc.subject.otherparallel computinges_ES
dc.titleMulti-Objective Big Data Optimization with jMetal and Sparkes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleEMO 2017es_ES
dc.relation.eventplaceMünster (Alemania)es_ES
dc.relation.eventdate19 marzo de 2017es_ES
dc.identifier.orcidhttp://orcid.org/0000-0001-5580-0484es_ES
dc.cclicenseby-nc-ndes_ES


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