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Evolution of topics and trends in emerging research fields: multiple analyses with entity linking, Mann–Kendall test and burst methods in cloud computing

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  • Mario Coccia

    (CNR, National Research Council of Italy, IRCRES-CNR, Turin Research Area of CNR)

  • Saeed Roshani

    (Amirkabir University of Technology)

Abstract

The principal goal of this study is to analyze the evolution of topics and trends in emerging research fields by a combination of entity linking, Mann–Kendall test, and burst detection techniques. Multiple methods are applied here in the emerging field of cloud computing by focusing on the frequency of critical topics from 2004 to 2021. Statistical analysis reveals that the Internet of Things exhibits a significant scientific growth compared to other subject areas within the research field of cloud computing. Other emerging topics with rapid growth are computer networks, encryption, big data, distributed computing, and interaction of cloud computing with virtual machine research. The combination of different techniques can better show the complex dynamics and complementary aspects of scientific topics and trends underlying evolutionary pathways in emerging fields, such as the science and technology advances of architecture, hardware, and software components in the field of cloud computing. In scientometrics, the analysis with multiple techniques provides comprehensive scientific and technological information driving new directions in the evolution of research fields to guide R&D investments towards growing topics and technologies having the potential of supporting fruitful scientific and technological change.

Suggested Citation

  • Mario Coccia & Saeed Roshani, 2024. "Evolution of topics and trends in emerging research fields: multiple analyses with entity linking, Mann–Kendall test and burst methods in cloud computing," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5347-5371, September.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:9:d:10.1007_s11192-024-05139-4
    DOI: 10.1007/s11192-024-05139-4
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    References listed on IDEAS

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    1. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    2. Melika Mosleh & Saeed Roshani & Mario Coccia, 2022. "Scientific laws of research funding to support citations and diffusion of knowledge in life science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1931-1951, April.
    3. 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.
    4. Mauricio Marrone, 2020. "Application of entity linking to identify research fronts and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 357-379, January.
    5. Mario Coccia, 2020. "The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 451-487, July.
    6. Mauricio Marrone & Sascha Lemke & Lutz M. Kolbe, 2022. "Entity linking systems for literature reviews," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 3857-3878, July.
    7. Hélène Dernis & Mariagrazia Squicciarini & Roberto Pinho, 2016. "Detecting the emergence of technologies and the evolution and co-development trajectories in science (DETECTS): a ‘burst’ analysis-based approach," The Journal of Technology Transfer, Springer, vol. 41(5), pages 930-960, October.
    8. Vivek Kumar Singh & Prashasti Singh & Mousumi Karmakar & Jacqueline Leta & Philipp Mayr, 2021. "The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5113-5142, June.
    9. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    10. 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.
    11. Zhongqiu Liu & Yaolin Liu & Yangjie Guo & Hua Wang, 2013. "Progress in global parallel computing research: a bibliometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 967-983, June.
    12. Mario Coccia & Barry Bozeman, 2016. "Allometric models to measure and analyze the evolution of international research collaboration," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1065-1084, September.
    13. Chen, Baitong & Tsutsui, Satoshi & Ding, Ying & Ma, Feicheng, 2017. "Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 11(4), pages 1175-1189.
    14. Mario Coccia, 2018. "General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1265-1283, November.
    15. Martina K Linnenluecke & Mauricio Marrone & Abhay K Singh, 2020. "Conducting systematic literature reviews and bibliometric analyses," Australian Journal of Management, Australian School of Business, vol. 45(2), pages 175-194, May.
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