Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15546
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dc.contributor.authorKhan, M-
dc.contributor.authorHuang, Z-
dc.contributor.authorLi, M-
dc.contributor.authorTaylor, GA-
dc.contributor.authorAshton, PM-
dc.contributor.authorKhan, M-
dc.date.accessioned2017-12-07T12:31:13Z-
dc.date.available2017-01-01-
dc.date.available2017-12-07T12:31:13Z-
dc.date.issued2017-
dc.identifier.citationMATHEMATICAL PROBLEMS IN ENGINEERING, (2017)en_US
dc.identifier.issnhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000415607100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=f12c8c83318cf2733e615e54d9ed7ad5-
dc.identifier.issnARTN 2198262-
dc.identifier.issnhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000415607100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=f12c8c83318cf2733e615e54d9ed7ad5-
dc.identifier.issnARTN 2198262-
dc.identifier.issn1024-123X-
dc.identifier.issn1563-5147-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/15546-
dc.description.abstractThe rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data fromPMUs. To that extent the Hadoop framework, an open source implementation of theMapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of theHadoop framework.This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.en_US
dc.format.extent? - ? (11)-
dc.languageEnglish-
dc.language.isoenen_US
dc.publisherHINDAWI LTDen_US
dc.subjectScience & Technologyen_US
dc.subjectTechnologyen_US
dc.subjectPhysical Sciencesen_US
dc.subjectEngineering, Multidisciplinaryen_US
dc.subjectMathematics, Interdisciplinary Applicationsen_US
dc.subjectEngineeringen_US
dc.subjectMathematicsen_US
dc.subjectMAPREDUCEen_US
dc.subjectDESIGNen_US
dc.titleOptimizing Hadoop Performance for Big Data Analytics in Smart Griden_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1155/2017/2198262-
dc.relation.isPartOfMATHEMATICAL PROBLEMS IN ENGINEERING-
pubs.publication-statusPublished-
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