From Traditional Database to Self-Tuning Database: A Paradigm Shift in Database Management

This research paper investigates the transformative transition from traditional database management systems (DBMS) to self-tuning databases that automate performance optimization, eliminating the need for extensive manual intervention by administrators. The evolution from static to dynamic database systems has been catalyzed by the integration of machine learning, advanced query optimization algorithms, and real-time memory management. Self-tuning databases are increasingly relied upon to handle high data volumes and complex queries efficiently. This paper explores the core components of self-tuning databases, the driving forces behind their development, their implications for performance, scalability, and administrative overhead reduction, as well as the challenges and future directions in this field.