Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/11832
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dc.contributor.authorDeng, C-
dc.contributor.authorLiu, Y-
dc.contributor.authorXu, L-
dc.contributor.authorYang, J-
dc.contributor.authorLiu, J-
dc.contributor.authorLi, S-
dc.contributor.authorLi, M-
dc.date.accessioned2016-01-13T10:38:41Z-
dc.date.available2015-08-28-
dc.date.available2016-01-13T10:38:41Z-
dc.date.issued2015-
dc.identifier.citationConcurrency Computation, 27, (6): pp.1375-1638, (2015)en_US
dc.identifier.issn1532-0626-
dc.identifier.issn1532-0634-
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1002/cpe.3580/epdf-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11832-
dc.description.abstractThe Common Information Model (CIM) has been heavily used in electric power grids for data exchange among a number of auxiliary systems such as communication systems, monitoring systems, and marketing systems. With a rapid deployment of digitalized devices in electric power networks, the volume of data continuously grows, which makes verification of CIM data a challenging issue. This paper presents a parallel K-means clustering algorithm for large-scale CIM data verification. The parallel K-means builds on the MapReduce computing model which has been widely taken up by the community in dealing with data-intensive applications. A genetic algorithm-based load-balancing scheme is designed to balance the workloads among the heterogeneous computing nodes for a further improvement in computation efficiency. The performance of the parallel K-means is initially evaluated in a small-scale in-house MapReduce cluster and subsequently evaluated in a commercial cloud computing platform. Finally, the parallel K-means is evaluated in large-scale simulated MapReduce environments. Both the experimental and simulation results show that the parallel K-means reduces the CIM data-verification time significantly compared with the sequential K-means clustering, while generating a high level of precision in data verification.en_US
dc.description.sponsorshipNational Science Foundation of China (no. 51437003), also National Basic Research Program (973) of China under grant no. 2014CB340404en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.subjectCIM verificationen_US
dc.subjectStochastic samplingen_US
dc.subjectClusteringen_US
dc.subjectMapReduceen_US
dc.subjectLoad balancingen_US
dc.titleA MapReduce-based parallel K-means clustering for large-scale CIM data verificationen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1002/cpe.3580-
dc.relation.isPartOfConcurrency Computation-
pubs.publication-statusAccepted-
pubs.publication-statusPublished-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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