IDEAS home Printed from https://ideas.repec.org/a/ags/asagre/265383.html
   My bibliography  Save this article

Risk Analysis of Big Data Based on Cloud Computing for the Inspection and Testing of Toxic and Hazardous Substances in Meat Products

Author

Listed:
  • WANG Yajie
  • HE Jinlin
  • WANG Peng
  • DAI Jiao
  • YANG Bing
  • TAN Hong
  • TAO Guangcan

Abstract

Large-scale data emerge in food safety inspection and testing industry with the development of Internet technology in China. This paper was aimed at designing toxic and hazardous substance big data risk analysis algorithm in food safety inspection and testing based on cloud computing [1]. Cloud computing platform was set up to store the massive extensive data with geographical distribution, dynamic and high complexity from the Internet, and MapReduce [2] computational framework in cloud computing was applied to process and compute parallel data. The risk analysis results were obtained by analyzing 1000000 meat products testing data collected from the laboratory management information system based on web. The results show that food safety index<1, which proves that the food safety state is in good condition.

Suggested Citation

  • WANG Yajie & HE Jinlin & WANG Peng & DAI Jiao & YANG Bing & TAN Hong & TAO Guangcan, 2017. "Risk Analysis of Big Data Based on Cloud Computing for the Inspection and Testing of Toxic and Hazardous Substances in Meat Products," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 9(08), August.
  • Handle: RePEc:ags:asagre:265383
    DOI: 10.22004/ag.econ.265383
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/265383/files/Risk%20Analysis%20of%20Big%20Data%20Based%20on%20Cloud%20Computing%20for%20the%20Inspection%20and%20Testing%20of%20Toxic%20and%20Hazardous%20Substances%20in%20Meat%20Products.PDF
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.265383?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    Agribusiness;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:asagre:265383. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.