Emerging trends and global scope of big data analytics: a scientometric analysis
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Emerging trends and global scope of big data analytics: a scientometric analysis Keshav Singh Rawat1 · Sandeep Kumar Sood1 Accepted: 14 October 2020 © Springer Nature B.V. 2020
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. Keywords Data analytics · Big data · Scientometric · Citation analysis · Emerging trends · VOSviewer
1 Introduction With today’s advanced systems and modern technologies like Cloud computing (Botta et al. 2016), and Internet of Things (IoT) (Ge et al. 2018), the massive amount of data are produced from different sources such as healthcare (Dash et al. 2019), social networks (Ghani et al. 2019), business marketing, finance (Erevelles et al. 2016), government * Keshav Singh Rawat [email protected] Sandeep Kumar Sood [email protected] 1
Department of Computer Science and Informatics, Central University of Himachal Pradesh, Dharamshala, India
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(Klievink et al. 2017), and education (Prinsloo and Slade 2017). Furthermore, by overwhelming use of smart phones, the multimedia data (audio, video and picture) have also developed massively. These large and complex data are referred as big data with the properties of high-volume, high velocity, and high variety (Attaran et al. 2018). Therefore, the method is required to extract hidden information from these large data for the formulation of various decisions and policies. Data analytics (DA) provides various techniques to analyze data set with specialized software tools to draw conclusions related to hidden information (Tsai et al. 2015). The knowledge discovery in databases (KDD) provides a way to extract hidden information from data sets using data mining steps and algorithms (Fayyad et al
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