Journal of Big Data, Vol. 4, No. 30, September 2017
23 Pages Posted: 3 Oct 2017
Date Written: September 29, 2017
The explosive growing number of data from mobile devices, social media, Internet of Things and other applications has highlighted the emergence of big data. This paper aims to determine the worldwide research trends on the field of big data and its most relevant research areas. A bibliometric approach was performed to analyse a total of 6572 papers including 28 highly cited papers and only papers that were published in the Web of Science™ Core Collection database from 1980 to 19 March 2015 were selected. The results were refined by all relevant Web of Science categories to computer science, and then the bibliometric information for all the papers was obtained. Microsoft Excel version 2013 was used for analyzing the general concentration, dispersion and movement of the pool of data from the papers. The t test and ANOVA were used to prove the hypothesis statistically and characterize the relationship among the variables. A comprehensive analysis of the publication trends is provided by document type and language, year of publication, contribution of countries, analysis of journals, analysis of research areas, analysis of web of science categories, analysis of authors, analysis of author keyword and keyword plus. In addition, the novelty of this study is that it provides a formula from multi-regression analysis for citation analysis based on the number of authors, number of pages and number of references.
Keywords: Big Data, Research Trends, Highly Cited Papers, Citation Analysis, Bibliometrics
JEL Classification: L11, L1, L2, M11, M12, M1, M54, Q1, O1, O3, P42, P24, P29, Q31, Q32, L17
Suggested Citation: Suggested Citation
Kalantari, Ali and Kamsin, Amirrudin and Kamaruddin, Halim Shukri and Ale Ebrahim, Nader and Gani, Abdullah and Ebrahimi, Ali and Shamshirband, Shahaboddin, A Bibliometric Approach to Tracking Big Data Research Trends (September 29, 2017). Journal of Big Data, Vol. 4, No. 30, September 2017. Available at SSRN: https://ssrn.com/abstract=3046448