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Visualizing the research on pervasive and ubiquitous computing

Author

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  • Rongying Zhao

    (Wuhan University)

  • Ju Wang

    (Wuhan University)

Abstract

The aim of this paper is to identify the research status quo on pervasive and ubiquitous computing via scientometric analysis. Information visualization and knowledge domain visualization techniques were adopted to determine how the study of pervasive and ubiquitous computing has evolved. A total of 5,914 papers published between 1995 and 2009 were retrieved from the Web of Science with a topic search of pervasive or ubiquitous computing. CiteSpace is a java application for analyzing and visualizing a wide range of networks from bibliographic data. By use of it, we generated the subject category network to identify the leading research fields, the research power network to find out the most productive countries and institutes, the journal co-citation map to identify the distribution of core journals, the author co-citation map to identify key scholars and their co-citation patterns, the document co-citation network to reveal the ground-breaking literature and detect the co-citation clusters on pervasive and ubiquitous computing, and depicted the hybrid network of keywords and noun phrases to explore research foci on pervasive and ubiquitous computing over the entire span 1995–2009.

Suggested Citation

  • Rongying Zhao & Ju Wang, 2011. "Visualizing the research on pervasive and ubiquitous computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(3), pages 593-612, March.
    • Handle: RePEc:spr:scient:v:86:y:2011:i:3:d:10.1007_s11192-010-0283-8
    DOI: 10.1007/s11192-010-0283-8
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    References listed on IDEAS

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    1. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    2. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    3. Chaomei Chen & Fidelia Ibekwe-SanJuan & Jianhua Hou, 2010. "The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1386-1409, July.
    4. Katherine W. McCain, 1986. "Cocited author mapping as a valid representation of intellectual structure," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 37(3), pages 111-122, May.
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    Cited by:

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    4. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    5. Dan Liu & Siqi Che & Wenzhong Zhu, 2022. "Visualizing the Knowledge Domain of Academic Mobility Research from 2010 to 2020: A Bibliometric Analysis Using CiteSpace," SAGE Open, , vol. 12(1), pages 21582440211, January.
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    7. Gohar Feroz Khan & Junghoon Moon & Han Woo Park, 2011. "Network of the core: mapping and visualizing the core of scientific domains," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 759-779, December.
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    9. Takano, Yasutomo & Kajikawa, Yuya, 2019. "Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 45-68.

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