• DocumentCode
    3207480
  • Title

    Constructing a Web-based Employee Training Expert System with Data Mining Approach

  • Author

    Chen, Kuang-Ku ; Chen, Mu-Yen ; Wu, Hui-Ju ; Lee, Yi-Lung

  • Author_Institution
    Nat. Changhua Univ. of Educ., Changhua
  • fYear
    2007
  • fDate
    23-26 July 2007
  • Firstpage
    659
  • Lastpage
    664
  • Abstract
    Knowledge management (KM) is an important strategy in business management and competition in 21st century. Companies must manage their valuable knowledge and experience more aggressively to enhance competitive advantage and human resource management (HRM). In this paper, we present a web-based training system named ETES - employee training expert system and the methodologies of its implementation. ETES applied rule-based expert system technology to infer the learning type for employees. Moreover, ETES uses association rule mining to find training strategies and learning map for personal learning. Besides, ETES provides different training materials for employees according to their learning aptitudes, records and occupations. The system has been tested and is now in pilot use by Teraauto Corporation which is a high-profits listed securities company in Taiwan.
  • Keywords
    computer aided instruction; data mining; expert systems; human resource management; knowledge management; Web-based employee training expert system; association rule mining; business management; data mining; human resource management; knowledge management; personal learning; rule-based expert system; Data mining; Engineering profession; Expert systems; Human resource management; Industrial training; Knowledge management; Learning; Management training; Systems engineering education; Technological innovation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    E-Commerce Technology and the 4th IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services, 2007. CEC/EEE 2007. The 9th IEEE International Conference on
  • Conference_Location
    Tokyo
  • Print_ISBN
    0-7695-2913-5
  • Type

    conf

  • DOI
    10.1109/CEC-EEE.2007.35
  • Filename
    4285283