Research Trends, Enabling Technologies and Application Areas for Big Data

Abstract

The availability of large amounts of data in combination with Big Data analytics has transformed many application domains. In this paper, we provide insights into how the area has developed in the last decade. First, we identify seven major application areas and six groups of important enabling technologies for Big Data applications and systems. Then, using bibliometrics and an extensive literature review of more than 80 papers, we identify the most important research trends in these areas. In addition, our bibliometric analysis also includes trends in different geographical regions. Our results indicate that manufacturing and agriculture or forestry are the two application areas with the fastest growth. Furthermore, our bibliometric study shows that deep learning and edge or fog computing are the enabling technologies increasing the most. We believe that the data presented in this paper provide a good overview of the current research trends in Big Data and that this kind of information is very useful when setting strategic agendas for Big Data research.

Bibliometric Mining of Research Directions and Trends for Big Data

“…This group has almost a decade of experience of working with Big Data research together with international partners and partners from industry and society. The group has published more than 150 research articles related to Big Data, some of these aimed at identifying trends and research directions [2][19] (more information about one of the large research projects done in the group can be found on https://a.bth.se/bigdata/).…”

“…for Big Data research. The group has done similar efforts before, but in that case without the support of a mining program but instead based on interviews with industry experts and senior international researchers in Big Data [2].…”

“…As can be expected, the high relevance of the Big Data area has triggered a large amount of research. A recent study of the trends and research directions in Big Data showed that there during the last 10 years there are more than 118 000 documents related to Big Data in the Scopus database alone, and that currently approximately 20 000 new documents are published every year [2] (i.e., more than 50 new documents published every day!). It is clearly very challenging for any researcher to stay up-to-date with the trends and directions in a research area with a production rate as high as that of Big Data.…”

Generative Adversarial Networks-based solution for improving medical data quality and insufficiency

“…In this era of big data, the application of data spans across nearly every aspect of life, bringing more intelligent solutions and innovations to various scenarios and achieving unexpected results. For instance, in the field of telecommunications, these data can facilitate operators in predicting and balancing network load; in the field of energy, meteorologists can predict the production of wind, light, and other renewable energy based on data such as weather and seasons [1]. The abundance of data allows individuals to glean fresh insights and observations that contribute to the advancement of societal infrastructure.…”