M.S. Candidate: Yeşim Dildar Korkmaz
Program: Information Systems
Date: 24.01.2023 / 11:00
Abstract: The advancements of High-Performance Computing (HPC), Big Data, Artificial Intelligence (AI) and Cloud Computing technologies has led to a convergence of these fields, resulting in the emergence of new significant improvements for a wide range of fields. Identifying the state of development of technology convergence and forecasting promising technology convergence is critical for both academia and industry. That's why technology assessment and forecasting for HPC-Big Data-AI-Cloud Computing convergence is needed. The purpose of this thesis is to evaluate the convergence of HPC with Big Data, AI and Cloud Computing technologies. In this thesis, bibliometric analysis approach is conducted, including performance analysis and network analysis to identify the research trends and themes for the convergence of these technologies. The results of the analysis reveal that there is a rapidly growing literature with a significant increase in research activities in this field in recent years. This study identifies key trends and patterns in the literature, including top published authors, most productive institutions, cited articles, and influential publications. In addition, research trends and thematic evolution analysis are carried out in this study. The existing studies that assess and forecast computational technologies do not consider the effect of convergence and do not apply bibliometric analysis in the field of HPC. This thesis provides valuable insights by identifying the bibliometric trends across the concept of technological convergence of HPC- Big Data-AI-Cloud Computing technologies.