Analysis of Big Data Research Hotspots Based on Keyword Co-occurrence

In the era of mobile Internet and cloud computing, the rise of big data has profoundly impacted diverse industries and sectors, drawing significant scholarly attention. The objective of this paper is to delineate an overview of the international research landscape of big data, identifying key research hotspots and emerging trends. To gain a comprehensive overview of the current state of research in the field of big data, we employed a range of analytical techniques, including co-word analysis, clustering analysis, social network analysis, and dynamic strategy coordinate mapping. Our review encompassed articles published between 2017 and 2019, spanning a three-year period. The data were sourced from the SCI, SSCI, SPCI, and CPCI-SSH databases. The literature was subjected to a sysytematic analysis using Bibexcel, SPSS, and Gephi software, with the objective of uncovering distinct patterns and trends. The findings reveal 11 thematic clusters within international big data research. These are text mining, data fusion, distributed computing, Industry 4.0, data privacy, business intelligence, precision medicine, bioinformatics, and data mining techniques such as classification and clustering. It is noteworthy that Industry 4.0, data privacy, and text mining emerged as significant research hotspots. Furthermore, the fields of artificial intelligence, natural language processing, smart grids, energy big data, and wireless sensor networks are regarded as emerging areas of interest. It is anticipated that the convergence of bioinformatics and other pivotal areas of big data research, will serve as the impetus for future research hotspots, necessitating sustained scholarly investigation and discourse.