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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.

Linked References

  1. J. T. Overpeck, G. A. Meehl, S. Bony, and D. R. Easterling, “Climate data challenges in the 21st century,” Science, vol. 331, no. 6018, pp. 700–702, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. J. Dean and S. Ghemawat, “MapReduce: simplied data processing on large clusters,” in Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI '04), pp. 1–6, San Francisco, Calif, USA, December 2004.
  3. X. Wu, V. Kumar, Q. J. Ross et al., “Top 10 algorithms in data mining,” Knowledge and Information Systems, vol. 14, no. 1, pp. 1–37, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Zhao, H. Ma, and Q. He, “Parallel K-means clustering based on MapReduce,” Cloud Computing, Springer, vol. 5931, pp. 674–679, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Vernica, M. J. Carey, and C. Li, “Efficient parallel set-similarity joins using MapReduce,” in Proceedings of the International Conference on Management of Data (SIGMOD '10), pp. 495–506, Indianapolis, Ind, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. L. Chao, Y. Yan, and R. Tonny, “A parallel Cop-Kmeans clustering algorithm based on MapReduce framework,” Advances in Intelligent and Soft Computing, vol. 123, pp. 93–102, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Ene, S. Im, and B. Moseley, “Fast clustering using MapReduce,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '11), pp. 681–689, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. H.-G. Li, G.-Q. Wu, X.-G. Hu, J. Zhang, A. Li, and X. Wu, “K-means clustering with bagging and MapReduce,” in Proceedings of the 44th Hawaii International Conference on System Sciences (HICSS '10), pp. 1–8, January 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. J.-H. Böse, A. Andrzejak, and M. Högqvist, “Beyond online aggregation: parallel and incremental data mining with online map-reduce,” in Proceedings of the Workshop on Massive Data Analytics on the Cloud (MDAC '10), pp. 1–6, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. C. T. Chu, S. K. Kim, Y. A. Lin et al., “Map reduce for machine learning on multicore,” in Advances in Neural Information Processing Systems 19, pp. 281–288, 2006.
  11. F. Chierichetti, R. Kumar, and A. Tomkins, “Max-cover in map-reduce,” in Proceedings of the 19th International World Wide Web Conference (WWW '10), pp. 231–240, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Clifton and K. J. Lundquist, “Data clustering reveals climate impacts on local wind phenomena,” Journal of Applied Meteorology and Climatology, vol. 51, pp. 1547–1557, 2012. View at Google Scholar
  13. G. Amit and D. Sara, “A survey on cloud computing,” Tech. Rep. CS 508, University of British Columbia, 2009. View at Google Scholar
  14. C. Ranger, R. Raghuraman, A. Penmetsa, G. Bradski, and C. Kozyrakis, “Evaluating MapReduce for multi-core and multiprocessor systems,” in Proceedings of the 13th IEEE International Symposium on High Performance Computer Architecture (HPCA '07), pp. 13–24, Phoenix, Ariz, USA, February 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Lämmel, “Google's MapReduce programming model - Revisited,” Science of Computer Programming, vol. 68, no. 3, pp. 208–237, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. B. He, W. Fang, Q. Luo, N. K. Govindaraju, and T. Wang, “Mars: a MapReduce framework on graphics processors,” in Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques (PACT '08), pp. 260–269, ACM, New York, NY,USA, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. T. White, Hadoop: The Definitive Guide, O'Reilly Media, Inc., 2009.
  19. X. Xu, J. Jäger, and H.-P. Kriegel, “A fast parallel clustering algorithm for large spatial databases,” Data Mining and Knowledge Discovery, vol. 3, no. 3, pp. 263–290, 1999. View at Google Scholar · View at Scopus