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Technology–function matrix based network analysis of cloud computing

Author

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  • Jia-Yen Huang

    (National Chin-Yi University of Technology)

  • Hung-Tu Hsu

    (National Chin-Yi University of Technology)

Abstract

This study aims to employ technology–function based patent analysis to identify the important technologies of cloud computing. This study exploits the Stanford parser and association rule to extract and separate the information concerning technologies and functions from patent text. Based on the results of the technology–function matrix, this study employs technology network analysis to investigate technology change. Moreover, this study proposes a technology–function matrix analysis diagram (TFMAD) and applies the technique for order preference by similarity to ideal solution to identify the most important technologies of cloud computing. Among the three classes of cloud computing, infrastructure as a service has the largest number of patents and the connections between patents are close, but the platform as a service has the highest patent growth rate. Based on the analysis of TFMAD, this study shows that technological developments related to computing device and virtual machines are of particular importance to the cloud computing industry.

Suggested Citation

  • Jia-Yen Huang & Hung-Tu Hsu, 2017. "Technology–function matrix based network analysis of cloud computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 17-44, October.
    • Handle: RePEc:spr:scient:v:113:y:2017:i:1:d:10.1007_s11192-017-2469-9
    DOI: 10.1007/s11192-017-2469-9
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    References listed on IDEAS

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    Cited by:

    1. Chiarello, Filippo & Fantoni, Gualtiero & Hogarth, Terence & Giordano, Vito & Baltina, Liga & Spada, Irene, 2021. "Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Jia-Yen Huang & Rong-Chang Chen, 2019. "Exploring the intellectual structure of cloud patents using non-exhaustive overlaps," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 739-769, November.
    3. Deborah Giustini, 2021. "The Impact Of Labour Market Trends On The Employment Of R&D Personnel: A Literature Review," HSE Working papers WP BRP 117/STI/2021, National Research University Higher School of Economics.

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