Meteorological Data Analysis Using MapReduce
In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/646497 |
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| _version_ | 1832548661460992000 |
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| author | Wei Fang V. S. Sheng XueZhi Wen Wubin Pan |
| author_facet | Wei Fang V. S. Sheng XueZhi Wen Wubin Pan |
| author_sort | Wei Fang |
| collection | DOAJ |
| description | In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-means) based on MapReduce according to characteristics of large meteorological datasets. The experimental results show that MK-means has more computing ability and scalability. |
| format | Article |
| id | doaj-art-2586c86de9fb4296ba258b5dded680b6 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-2586c86de9fb4296ba258b5dded680b62025-02-03T06:13:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/646497646497Meteorological Data Analysis Using MapReduceWei Fang0V. S. Sheng1XueZhi Wen2Wubin Pan3Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaComputer Science Department, University of Central Arkansas, Conway, AR 72035, USASchool of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaIn the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-means) based on MapReduce according to characteristics of large meteorological datasets. The experimental results show that MK-means has more computing ability and scalability.http://dx.doi.org/10.1155/2014/646497 |
| spellingShingle | Wei Fang V. S. Sheng XueZhi Wen Wubin Pan Meteorological Data Analysis Using MapReduce The Scientific World Journal |
| title | Meteorological Data Analysis Using MapReduce |
| title_full | Meteorological Data Analysis Using MapReduce |
| title_fullStr | Meteorological Data Analysis Using MapReduce |
| title_full_unstemmed | Meteorological Data Analysis Using MapReduce |
| title_short | Meteorological Data Analysis Using MapReduce |
| title_sort | meteorological data analysis using mapreduce |
| url | http://dx.doi.org/10.1155/2014/646497 |
| work_keys_str_mv | AT weifang meteorologicaldataanalysisusingmapreduce AT vssheng meteorologicaldataanalysisusingmapreduce AT xuezhiwen meteorologicaldataanalysisusingmapreduce AT wubinpan meteorologicaldataanalysisusingmapreduce |