• DocumentCode
    3233264
  • Title

    Using MapReduce for Data Processing in the Cloud for Forest Pest Control

  • Author

    Jiang, Shaocan ; Shen, Chaofan ; Xiao, Yongjie ; Huang, Xiaoying

  • Author_Institution
    Sch. of Inf. Eng., Zhejiang A&F Univ., Lin´´an, China
  • fYear
    2010
  • fDate
    21-24 Oct. 2010
  • Firstpage
    324
  • Lastpage
    327
  • Abstract
    Forest pests, fires and deforestation, are known as the three forest disasters. And the severity of forest pest infection has increased in recent decades. Therefore, it´s becoming more important to have strategic approach toward forest pest control. After decades of research, we have accumulated and summed up a wealth of forest pest data. We need to process these data efficiently, and make sure that forest pest data are better service to pest control work. Here, we try to set up a platform for forest pest control which is based on cloud computing. And as a programming method in the cloud, MapReduce destines to be an ideal data processing framework in handling the huge amount of pest data. In this paper, we build up a data processing framework as one part of the cloud computing platform for forest pest control. This framework mainly includes 3 modules: HDFS stores Geo-data, HBase stores Attribute-Data, and MapReduce processes data.
  • Keywords
    Internet; control engineering computing; pest control; HBase; HDFS; MapReduce; cloud computing; data processing framework; forest disasters; forest pest control; forest pest infection; programming method; Cloud computing; Data processing; Diseases; Forecasting; Memory; Pest control; Servers; Cloud Computing; Data Processing; Forest Pests; MapReduce;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networking and Distributed Computing (ICNDC), 2010 First International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4244-8382-2
  • Type

    conf

  • DOI
    10.1109/ICNDC.2010.88
  • Filename
    5645367