Sunday, 1 October 2017

A bibliometric approach to tracking big data research trends | SpringerLink

 Source: https://link.springer.com/article/10.1186/s40537-017-0088-1

Journal of Big Data

, 4:30 | Cite as

A bibliometric approach to tracking big data research trends

  • Ali Kalantari
  • Amirrudin Kamsin
  • Halim Shukri Kamaruddin
  • Nader Ale Ebrahim
  • Abdullah Gani
  • Ali Ebrahimi
  • Shahaboddin Shamshirband
  • Ali Kalantari
    • 1
  • Amirrudin Kamsin
    • 1
  • Halim Shukri Kamaruddin
    • 2
  • Nader Ale Ebrahim
    • 3
  • Abdullah Gani
    • 1
  • Ali Ebrahimi
    • 1
  • Shahaboddin Shamshirband
    • 4
    • 5
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Actuarial and Applied Statistics, Faculty of Business & Information ScienceUSCI UniversityKuala LumpurMalaysia
  3. 3.Centre for Research Services, Institute of Research Management and Monitoring (IPPP)University of Malaya (UM)Kuala LumpurMalaysia
  4. 4.Department for Management of Science and Technology DevelopmentTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
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Abstract

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.

Keywords

Big data Research trends Highly cited papers Citation analysis 


A bibliometric approach to tracking big data research trends | SpringerLink

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