Exploring the Architecture and Functions of Intelligent Database Management Systems
Xiaozhong Wang, Yangyang Wang, Guangchao Deng, Yunzhi Shi · 2024
This paper explores the architecture and functions of Intelligent Database Management Systems (IDBMS), which integrate advanced artificial intelligence (AI) technologies to enhance traditional database management. By addressing the limitations of conventional systems, IDBMS aim to improve query optimization, resource utilization, and user interaction through machine learning, predictive analytics, and natural language processing. The paper outlines the core components and architectural models of IDBMS, details their functionalities, presents case studies demonstrating their effectiveness, and discusses future trends and challenges in the field.