Big Data Research Outputs in the Library and Information Science: South African’s Contribution using Bibliometric Study of Knowledge Production

  • Patrick Ajibade
  • Stephen M. Mutula
Keywords: Data Archiving, Bibliometrics, Big Data,Controlled Index Terms, Metadata Management,Faceted Classification, Web Archiving

Abstract

The focus of this study was to evaluate research production in Library and Information  Science (LIS) on big data and South African’s contribution from 1992-2019. As advancement in technological innovation is changing the methods of digital collection development and dissemination of information in the fourth industrial  revolution, big data technology will be reshaping library management systems through big data. Big data is defined as information overload due to the volume, varieties, velocity and veracity of the data which must be processed to get value. It is also useful information for efficient decision making or business intelligence.
The data collection methods utilised bibliometric analysis as an intuitive approach to map research focus in big data and LIS contribution, by visualising the outputs using data harvesting capability of Web of Knowledge to export titles, authors, abstract, all keywords, citations, journal sources and bibliographies for further analysis.
We performed bibliometric coupling, co-citation analysis, with a total dataset (n = 8,415), h-index =104, and an average citation per output (ACP=97). The findings showed that the LIS scholars contributions were very low (h-index = 29) and (ACI =15.47), and the USA (n=112,) China (n=45) and India (n=25) were the top leading countries in LIS and big data. The contribution of South Africa was very low (n=4). This research underscores that LIS big data contribution is very important for archiving and providing information services to manage petabytes data and information with automated controlled index terms and big data metadata management.

Keywords: Data Archiving, Bibliometrics, Big Data,
Controlled Index Terms, Metadata Management,
Faceted Classification, Web Archiving

Published
2020-06-04
Section
Articles

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eISSN: 0795-4778