Bibliometric Mining of Research Directions and Trends for Big Data

Abstract

In this paper a program and methodology for bibliometric mining of research trends and directions is presented. The method is applied on the research area Big Data for the time period 2012 to 2021, 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. For four geographical regions (North America, European Union, China, and The Rest of the World) we investigate different activity levels in Big Data during different parts of the time period. 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 geographical regions and the research direction Review has the highest average citation score among the research directions. The program and a methodology for bibliometric mining developed in this study can be used also for other large research areas than Big Data. Now that the program and methodology have been developed, one could probably perform a similar study in some other research area in a couple of days.