IDEAS home Printed from https://ideas.repec.org/a/igg/jitwe0/v15y2020i4p1-17.html
   My bibliography  Save this article

A Novel Algorithm for Distributed Data Stream Using Big Data Classification Model

Author

Listed:
  • Yongxiao Qiu

    (Hebei Finance University, China)

  • Guanghui Du

    (Hebei Finance University, China)

  • Song Chai

    (China Mobile Group Hebei Co. Ltd., Baoding, China)

Abstract

In order to solve the problem of real-time detection of power grid equipment anomalies, this paper proposes a data flow classification model based on distributed processing. In order to realize distributed processing of power grid data flow, a local node mining method and a global mining mode based on uneven data flow classification are designed. A data stream classification model based on distributed processing is constructed, then the corresponding data sequence is selected and formatted abstractly, and the local node mining method and global mining mode under this model are designed. In the local node miner, the block-to-block mining strategy is implemented by acquiring the current data blocks. At the same time, the expression and real-time maintenance of local mining patterns are completed by combining the clustering algorithm, thus improving the transmission rate of information between each node and ensuring the timeliness of the overall classification algorithm.

Suggested Citation

  • Yongxiao Qiu & Guanghui Du & Song Chai, 2020. "A Novel Algorithm for Distributed Data Stream Using Big Data Classification Model," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 15(4), pages 1-17, October.
    • Handle: RePEc:igg:jitwe0:v:15:y:2020:i:4:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJITWE.2020100101
    Download Restriction: no
    ---><---

    More about this item

    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:igg:jitwe0:v:15:y:2020:i:4:p:1-17. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.