The research landscape of big data: a bibliometric analysis

Xiaohong Liu, Ruiqing Sun, Shiyun Wang, Yenchun Jim Wu

Research output: Contribution to journalArticle

Abstract

Purpose: In recent years, the rapid growth of big data has presented immense potential for business applications as well as raised great interest from academia. In response to this emerging phenomenon, the purpose of this paper is to provide a comprehensive literature review of big data. Design/methodology/approach: A bibliometric method was used to analyze the articles obtained from the Scopus database published between 2013 and 2018. A sample size of 4,070 articles was evaluated using SciVal metrics. Findings: The analysis revealed an array of interesting findings as follows: the number of publications related to big data increased steadily over the past six years, though the rate of increase has slowed since 2014; the scope of big data research is quite broad in regards to both research domains and countries; despite a large volume of publications, the overall performance of big data research is not well presented as measured by the field-weighted citation impact metric; collaboration between different institutions, particularly in the form of international collaboration and academic–corporate collaboration, has played an important role in improving the performance of big data research. Originality/value: To the best of the authors’ knowledge, this is the first study to provide a holistic view of the big data research. The insights obtained from the analysis are instrumental for both academics and practitioners.

Original languageEnglish
JournalLibrary Hi Tech
DOIs
Publication statusAccepted/In press - 2019 Jan 1
Externally publishedYes

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Keywords

  • Bibliometric analysis
  • Big data
  • Data analysis
  • Data mining
  • Literature review
  • SciVal metrics

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences

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