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Keywords co-occurrence mapping knowledge domain research base on the theory of Big Data in oil and gas industry

Author

Listed:
  • Lin Zhu

    (Southwest Petroleum University)

  • Xiantao Liu

    (Southwest Petroleum University)

  • Sha He

    (Southwest Petroleum University)

  • Jun Shi

    (Southwest Petroleum University)

  • Ming Pang

    (Southwest Petroleum University)

Abstract

Taking the theses’ keywords in China from 1986 to 2014 as the research materials, use the basis concept of the Big Data Theory to further study the keywords which related to oil and gas industry. Analyze the keywords frequency of the theses in oil and gas industry and its co-occurrence frequency pair, and then use the theory of mapping knowledge domain to visualize the keywords co-occurrence network in petroleum industry so as to make further research of the heated issues that mapping knowledge domain has shown. According to the research we can see that the application technology R&D (research and development) predominate the oil and gas industry, featuring a high concentration and long tail phenomenon (which means various researches focus on different kinds of things, the scale of the research is large).

Suggested Citation

  • Lin Zhu & Xiantao Liu & Sha He & Jun Shi & Ming Pang, 2015. "Keywords co-occurrence mapping knowledge domain research base on the theory of Big Data in oil and gas industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 249-260, October.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:1:d:10.1007_s11192-015-1658-7
    DOI: 10.1007/s11192-015-1658-7
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    References listed on IDEAS

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    1. Chaim Zins & Plácida L.V.A.C. Santos, 2011. "Mapping the knowledge covered by library classification systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(5), pages 877-901, May.
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    3. Limei Zhao & Qingpu Zhang, 2011. "Mapping knowledge domains of Chinese digital library research output, 1994–2010," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(1), pages 51-87, October.
    4. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    5. Chaim Zins & Plácida L.V.A.C. Santos, 2011. "Mapping the knowledge covered by library classification systems," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(5), pages 877-901, May.
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    Cited by:

    1. Sara Sassetti & Giacomo Marzi & Vincenzo Cavaliere & Cristiano Ciappei, 2018. "Entrepreneurial cognition and socially situated approach: a systematic and bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1675-1718, September.
    2. Giacomo Marzi & Marina Dabić & Tugrul Daim & Edwin Garces, 2017. "Product and process innovation in manufacturing firms: a 30-year bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 673-704, November.
    3. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.

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