A bibliometric approach to tracking big data research trends

被引：66
作者：
Kalantari A. [1 ]
Kamsin A. [1 ]
Kamaruddin H.S. [2 ]
Ale Ebrahim N. [3 ]
Gani A. [1 ]
Ebrahimi A. [1 ]
Shamshirband S. [4 ,5 ]
机构：
[1] Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur
[2] Department of Actuarial and Applied Statistics, Faculty of Business & Information Science, USCI University, Kuala Lumpur
[3] Centre for Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya (UM), Kuala Lumpur
[4] Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City
[5] Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City
来源：
Shamshirband, Shahaboddin (shahaboddin.shamshirband@tdt.edu.vn) | 1600年 / SpringerOpen卷 / 04期
关键词：
Big data; Citation analysis; Highly cited papers; Research trends;
D O I：
10.1186/s40537-017-0088-1
中图分类号：
学科分类号：
摘要：
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 ScienceTM 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. © 2017, The Author(s).
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