Bibliometric Analysis and Knowledge Mapping of Research Hotspots in Data Middle Platform Architecture
Yunlong Gao, Meiran Zhang, WanZhu ZHANG · 2024
This research employs bibliometric methods and VOSviewer software to conduct a thorough analysis of academic literature in the key technical field of data middle platform architecture. Utilizing the Web of Science Core Collection database, this study has retrieved and analyzed 625 relevant documents from 1900 to 2024, uncovering the developmental trends and academic focal points in the research of data middle platform architecture. The findings reveal that disciplines such as Computer Science and Information Systems, Health Care Sciences and Services, and Environmental Sciences have made significant contributions in this domain. Institutions like the University of Oxford (Univ Oxford), the University of Toronto (Univ Toronto), and Indiana University (Indiana Univ) are recognized for their considerable academic influence. The keyword co-occurrence network analysis highlights core topics such as "data management," "cloud computing," "big data," and "data governance," while also showcasing the potential application of emerging technologies like "machine learning," "artificial intelligence," and "blockchain" within the data middle platform architecture. The international cooperation network analysis indicates active collaboration in data middle technology research among countries including the United Kingdom (UK), Norway, Iceland, and Mexico. This study offers new perspectives and insights into understanding the academic frontier, developmental trajectory, and research hotspots of data middle platform architecture, providing a foundation for knowledge accumulation and theoretical advancement in this field.