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Emerging trends and global scope of big data analytics: a scientometric analysis

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  • Keshav Singh Rawat

    (Central University of Himachal Pradesh)

  • Sandeep Kumar Sood

    (Central University of Himachal Pradesh)

Abstract

The primary sources of big data nowadays are from cloud computing, social networks, and the internet of things, and henceforth the data analytics has gained popularity these days, with the increasing demand for these technologies. This study presents scientometric analysis to identify overall growth, emerging trends, and global scope of data analytics research during 2010–2019. This study uses a bibliometric database retrieved from the Scopus in CSV files that contain bibliographic information. This study provides a detailed look of bibliometric features of Scopus indexed documents and analyses bibliometric networks to identify the hidden information from the downloaded dataset. This study focuses on the research publication growth, subject categories, geographical distribution, citation, and productivity parameters of bibliometric data. Furthermore, it identifies significant major contributors, highly cited publications, prominent journals, influential institutes, and research collaborations. This study also reveals research frontiers,and hotspots of data analytics research by analyzing keyword co-occurrence using VOSviewer. The outcomes of this study present the applications, emerging trends, and global research landscape over the last decade that help to understand fundamental research and the directions of future research in this field.

Suggested Citation

  • Keshav Singh Rawat & Sandeep Kumar Sood, 2021. "Emerging trends and global scope of big data analytics: a scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1371-1396, August.
    • Handle: RePEc:spr:qualqt:v:55:y:2021:i:4:d:10.1007_s11135-020-01061-y
    DOI: 10.1007/s11135-020-01061-y
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    Cited by:

    1. Stefania Capogna, 2023. "Sociology between big data and research frontiers, a challenge for educational policies and skills," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 193-212, February.
    2. Krunal K. Punjani & Kala Mahadevan & Angappa Gunasekaran & V. V. Ravi Kumar & Sujata Joshi, 2023. "Cloud computing in agriculture: a bibliometric and network visualization analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3849-3883, August.
    3. Sepideh Fahimifar & Khadijeh Mousavi & Fatemeh Mozaffari & Marcel Ausloos, 2023. "Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3685-3712, August.

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