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, vol. 2017, Article ID 2198262, 11 pages, 2017en_US
dc.identifier.issn1024-123X-
dc.identifier.urihttps://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.subjectconfiguration parametersen_US
dc.subjectdetrended fluctuation analysisen_US
dc.subjectgene expression programmingen_US
dc.subjectHadoop frameworksen_US
dc.subjectopen source implementationen_US
dc.subjectphasor measurement unit (PMUs)en_US
dc.subjectrapid deploymentsen_US
dc.subjectMapReduceen_US
dc.subjectsmart grid applicationsen_US
dc.titleOptimizing Hadoop Performance for Big Data Analytics in Smart Griden_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1155/2017/2198262-
dc.relation.isPartOfMathematical Problems in Engineering-
pubs.publication-statusPublished-
dc.identifier.eissn1563-5147-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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