• DocumentCode
    3304208
  • Title

    Machine Learning Methodology for Enhancing Automated Process in IT Incident Management

  • Author

    Li, Haochen ; Zhan, Zhiqiang

  • Author_Institution
    State Key Lab. of Networking & Switching, Beijing Univ. of Posts & Telecommun., Beijing, China
  • fYear
    2012
  • fDate
    23-25 Aug. 2012
  • Firstpage
    191
  • Lastpage
    194
  • Abstract
    Operating system experienced a rise in number of incidents in recent years. Analysis and reemployment of past solution therefore may make a contribution in reducing service interrupt time and minimizing business losses. The training and retaining of human resources is another primary disbursement source for enterprise. Thus, it is of great significance for enterprises to find reasonable solutions automatically. Combined with keyword tokenization, data mining, numerical optimization and neural network, this paper presents a system that compares and finds the most similar incident solution in the past, based on the description provided by customers in natural language. We try to improve the automated process by increasing the efficiency and accuracy through machine learning methodology and also devote to presenting a practical decision support method.
  • Keywords
    business data processing; data mining; human resource management; learning (artificial intelligence); natural language processing; neural nets; IT incident management; automated process; business loss; data mining; decision support method; enterprise; human resource retaining; human resource training; keyword tokenization; machine learning methodology; natural language; neural network; numerical optimization; operating system; service interrupt time; Accuracy; Biological neural networks; Computer architecture; Machine learning; Optimization; Training; IT incident management; data mining; neural network; numerical optimization; tokenize;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Network Computing and Applications (NCA), 2012 11th IEEE International Symposium on
  • Conference_Location
    Cambridge, MA
  • Print_ISBN
    978-1-4673-2214-0
  • Type

    conf

  • DOI
    10.1109/NCA.2012.28
  • Filename
    6299094