Oracle Autonomous Database for Transforming Database Administration by Harnessing Artificial Intelligence

In the modern era of big data and cloud computing, efficient database management systems (DBMS) handle vast amounts of structured and unstructured data in organizations. Traditional database administration requires continuous human intervention for performance tuning, security patching and resource scaling, leading to high operational costs and potential human errors. Oracle Autonomous Database (OAD) addresses these challenges by utilizing Artificial Intelligence (AI) and Machine Learning (ML) to automate key administrative tasks, enabling self-driving, self-securing and self-repairing capabilities. This study introduces a Reinforcement Learning (RL)-based approach for optimizing query execution in OAD. The proposed RL model dynamically adapts to workload variations, minimizing execution time and optimizing resource utilization. Experimental results validate the effectiveness of RL-based query optimization, showing a 38.9% reduction in execution time from an average of 1.8 seconds to 1.1 seconds per query. Resource consumption metrics also improved significantly, with CPU usage decreasing by 27.1%, memory consumption reducing by 23.3% and disk I/O operations improving by 31.4%. Additionally, query throughput increased by 40% demonstrating enhanced database efficiency. These findings highlight the transformative impact of AI-driven automation in database administration, making OAD a viable solution for enterprises seeking efficiency, cost savings and high-performance data management.