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How China Deals with Big Data

Author

Listed:
  • Yong Shi

    (University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Zhiguang Shan

    (State Information Center)

  • Jianping Li

    (Chinese Academy of Sciences)

  • Yufei Fang

    (University of Science and Technology
    State Information Center)

Abstract

On September 5, 2015, the State Council of Chinese Government, China’s cabinet formally announced its Action Framework for Promoting Big Data ( www.gov.cn , 2015). This is the milestone for China to catch up the global wave of big data. Since 2012 big data became a hot issue for scientific communities as well as the governments of many countries (Lazer et al. in Science 343:1203–1205, 2014; Einav et al. in Science 345:715, 2014; Cate in Science 346:818, 2014; Khoury and Ioannidis in Science 346:1054–1055, 2014). At the 2013 G8 Summit, the leaders of Canada, France, Germany, Italy, Japan, Russia, U.S.A. and United Kingdom agreed on an “open government plan” ( www.gov.uk/government/publications/open-data-charter/g8-open-data-charter-and-technical-annex , 2013). China’s framework, however, mainly emphasizes the integration of all trans-departmental data and establishes a number of government-driven national big data platforms so as to provide big data services to research, public and enterprises. The framework not only demonstrates a strong commitment of the Chinese government on big data, but also covers a wide range of governmental branches, enterprises and institutions far more than that of other countries. In addition, the framework shows an interpretation of big data that differs from other countries. If its objective is achieved, China would become a strong “big data country”.

Suggested Citation

  • Yong Shi & Zhiguang Shan & Jianping Li & Yufei Fang, 2017. "How China Deals with Big Data," Annals of Data Science, Springer, vol. 4(4), pages 433-440, December.
    • Handle: RePEc:spr:aodasc:v:4:y:2017:i:4:d:10.1007_s40745-017-0129-9
    DOI: 10.1007/s40745-017-0129-9
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    Citations

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    Cited by:

    1. B. Shravan Kumar & Vadlamani Ravi & Rishabh Miglani, 2021. "Predicting Indian Stock Market Using the Psycho-Linguistic Features of Financial News," Annals of Data Science, Springer, vol. 8(3), pages 517-558, September.
    2. Emmanuel Afuecheta & Chigozie Utazi & Edmore Ranganai & Chibuzor Nnanatu, 2023. "An Application of Extreme Value Theory for Measuring Financial Risk in BRICS Economies," Annals of Data Science, Springer, vol. 10(2), pages 251-290, April.
    3. Braznev Sarkar & Malay Bhattacharyya, 2021. "Spectral Algorithms for Streaming Graph Analysis: A Survey," Annals of Data Science, Springer, vol. 8(4), pages 667-681, December.
    4. Sanjay Kumar, 2020. "Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis," Annals of Data Science, Springer, vol. 7(3), pages 417-425, September.
    5. Atanu Bhattacharjee, 2020. "Estimation of Treatment Effect with Missing Observations for Three Arms and Three Periods Crossover Clinical Trials," Annals of Data Science, Springer, vol. 7(3), pages 447-460, September.
    6. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.

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