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Research On Data Mining And Knowledge Management And Its Applications In China'S Economic Development: Significance And Trend

Author

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  • SIWEI CHENG

    (School of Management, Graduate University of Chinese Academy of Sciences, Beijing 100080, China)

  • RUWEI DAI

    (Institute of Automation, Chinese Academy of Sciences, Beijing 100083, China)

  • WEIXUAN XU

    (Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100083, China)

  • YONG SHI

    (Chinese Academy of Sciences Research Center on Data, Technology & Knowledge Economy, Graduate University of Chinese Academy of Sciences, Beijing 100080, China)

Abstract

From 1978 to 2004, China's GDP growth rate has been 9.4% annually. In 2005, China's GDP growth rate was 9.9%, which at $2,278 billion currently is ranked number four in the world. This paper discusses the significance of transforming research findings in data mining and knowledge management (DMKM) to support China's economic development as well as the trend of DMKM applications in China. It first reviews development of DTKM in international communities and industries, which includes academic activities and business applications tools. Second, this paper outlines the significance and needs of DTKM in China's economic development, as to promote further DMKM research, innovatively produce new business intelligence tools, and effectively adopt existing commercial software packages. Finally, the paper observes some problems and challenges of using DMKM to support China's economy.

Suggested Citation

  • Siwei Cheng & Ruwei Dai & Weixuan Xu & Yong Shi, 2006. "Research On Data Mining And Knowledge Management And Its Applications In China'S Economic Development: Significance And Trend," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 585-596.
    • Handle: RePEc:wsi:ijitdm:v:05:y:2006:i:04:n:s021962200600226x
    DOI: 10.1142/S021962200600226X
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    Cited by:

    1. Qingxin Meng & Keli Xiao & Dazhong Shen & Hengshu Zhu & Hui Xiong, 2022. "Fine-Grained Job Salary Benchmarking with a Nonparametric Dirichlet Process–Based Latent Factor Model," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2443-2463, September.

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