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DC Field | Value | Language |
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dc.contributor.author | Deng, C | - |
dc.contributor.author | Liu, Y | - |
dc.contributor.author | Xu, L | - |
dc.contributor.author | Yang, J | - |
dc.contributor.author | Liu, J | - |
dc.contributor.author | Li, S | - |
dc.contributor.author | Li, M | - |
dc.date.accessioned | 2016-01-13T10:38:41Z | - |
dc.date.available | 2015-08-28 | - |
dc.date.available | 2016-01-13T10:38:41Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Concurrency Computation, 27, (6): pp.1375-1638, (2015) | en_US |
dc.identifier.issn | 1532-0626 | - |
dc.identifier.issn | 1532-0634 | - |
dc.identifier.uri | http://onlinelibrary.wiley.com/doi/10.1002/cpe.3580/epdf | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/11832 | - |
dc.description.abstract | The 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.sponsorship | National Science Foundation of China (no. 51437003), also National Basic Research Program (973) of China under grant no. 2014CB340404 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Wiley | en_US |
dc.subject | CIM verification | en_US |
dc.subject | Stochastic sampling | en_US |
dc.subject | Clustering | en_US |
dc.subject | MapReduce | en_US |
dc.subject | Load balancing | en_US |
dc.title | A MapReduce-based parallel K-means clustering for large-scale CIM data verification | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/cpe.3580 | - |
dc.relation.isPartOf | Concurrency Computation | - |
pubs.publication-status | Accepted | - |
pubs.publication-status | Published | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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