4.5 Article

Bibliometric mining of research directions and trends for big data

期刊

JOURNAL OF BIG DATA
卷 10, 期 1, 页码 -

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SPRINGERNATURE
DOI: 10.1186/s40537-023-00793-6

关键词

Bibliometrics; Research directions; Research trends; Fields of science and technology; Geographic regions; Scopus database

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This paper presents a program and methodology for bibliometric mining of research trends and directions. The method is applied to the research area of Big Data from 2012 to 2022 using the Scopus database. The top 10 research directions in Big Data are identified, as well as the role of Big Data research in various fields. Analysis is also conducted on the activity levels of different geographic regions and the citation scores of documents from different regions and research directions.
In this paper a program and methodology for bibliometric mining of research trends and directions is presented. The method is applied to the research area Big Data for the time period 2012 to 2022, using the Scopus database. It turns out that the 10 most important research directions in Big Data are Machine learning, Deep learning and neural networks, Internet of things, Data mining, Cloud computing, Artificial intelligence, Healthcare, Security and privacy, Review, and Manufacturing. The role of Big Data research in different fields of science and technology is also analysed. For four geographic regions (North America, European Union, China, and The Rest of the World) different activity levels in Big Data during different parts of the time period are analysed. North America was the most active region during the first part of the time period. During the last years China is the most active region. The citation scores for documents from different regions and from different research directions within Big Data are also compared. North America has the highest average citation score among the geographic regions and the research direction Review has the highest average citation score among the research directions. The program and methodology for bibliometric mining developed in this study can be used also for other large research areas. Now that the program and methodology have been developed, it is expected that one could perform a similar study in some other research area in a couple of days.

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