Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/60417

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dc.contributor.authorRodrigues, Máriopor
dc.contributor.authorSantos, Maribel Yasminapor
dc.contributor.authorBernardino, Jorgepor
dc.date.accessioned2019-05-28T13:20:32Z-
dc.date.issued2019-
dc.identifier.issn1942-4787-
dc.identifier.urihttp://hdl.handle.net/1822/60417-
dc.description.abstractBig Data is currently a hot topic of research and development across several business areas mainly due to recent innovations in information and communication technologies. One of the main challenges of Big Data relates to how one should efficiently handle massive volumes of complex data. Due to the notorious complexity of the data that can be collected from multiple sources, usually motivated by increasing data volumes gathered at high velocity, efficient processing mechanisms are needed for data analysis purposes. Motivated by the rapid growth in technology, development of tools, and frameworks for Big Data, there is much discussion about Big Data querying tools and, specifically, those that are more appropriated for specific analytical needs. This paper describes and evaluates the following popular Big Data processing tools: Drill, HAWQ, Hive, Impala, Presto, and Spark. An experimental evaluation using the Transaction Processing Council (TPC-H) benchmark is presented and discussed, highlighting the performance of each tool, according to different workloads and query types. This article is categorized under: Technologies > Computer Architectures for Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Data Preprocessing Application Areas > Data Mining Software Tools.por
dc.description.sponsorshipFCT – Fundação para a Ciência e Tecnologia, Grant/Award Number: UID/CEC/00319/2013; COMPETE, Grant/Award Number: POCI01-0145-FEDER-007043por
dc.language.isoengpor
dc.publisherWiley-Blackwellpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsrestrictedAccesspor
dc.subjectBig Datapor
dc.subjectBig Data analyticspor
dc.subjectquery processingpor
dc.subjectSQL-on-Hadooppor
dc.titleBig data processing tools: An experimental performance evaluationpor
dc.typearticlepor
dc.peerreviewedyespor
oaire.citationIssue2por
oaire.citationIssue2por
oaire.citationVolume9por
oaire.citationVolume9por
dc.date.updated2019-05-09T15:09:55Z-
dc.identifier.doi10.1002/widm.1297por
dc.date.embargo10000-01-01-
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
dc.subject.wosScience & Technologypor
sdum.journalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discoverypor
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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