Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 646497, 10 pages
http://dx.doi.org/10.1155/2014/646497
Research Article

Meteorological Data Analysis Using MapReduce

1Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China
2School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
3Computer Science Department, University of Central Arkansas, Conway, AR 72035, USA

Received 27 October 2013; Accepted 6 January 2014; Published 23 February 2014

Academic Editors: L. Koczy and S.-S. Liaw

Copyright © 2014 Wei Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.