AI-Driven autonomous database management: Self-tuning, predictive query optimization, and intelligent indexing in enterprise it environments

Oloruntoba, Oluwafemi (2025) AI-Driven autonomous database management: Self-tuning, predictive query optimization, and intelligent indexing in enterprise it environments. World Journal of Advanced Research and Reviews, 25 (2). pp. 1558-1580. ISSN 2581-9615

The rapid growth of enterprise data and the increasing complexity of modern database systems have necessitated a shift from traditional manual database management to autonomous, AI-driven solutions. AI-driven autonomous database management systems (ADBMS) leverage machine learning, predictive analytics, and automation to optimize database performance, reduce administrative overhead, and enhance scalability in enterprise IT environments. Traditional database management approaches often suffer from inefficiencies related to query performance, indexing, workload tuning, and anomaly detection, leading to increased operational costs and performance bottlenecks. This paper explores the key components of AI-driven autonomous database management, focusing on self-tuning mechanisms, predictive query optimization, and intelligent indexing techniques. Self-tuning capabilities leverage AI to analyze workloads, optimize resource allocation, and dynamically adjust system parameters to maintain peak efficiency. Predictive query optimization utilizes deep learning algorithms to enhance query execution plans, reduce latency, and anticipate performance issues before they impact business operations. Additionally, intelligent indexing applies machine learning techniques to automate index selection, adaptation, and maintenance, ensuring optimal data retrieval and reducing query processing times. By integrating these AI-driven mechanisms, enterprises can achieve greater operational efficiency, improved database reliability, and reduced human intervention in performance tuning. The study also addresses security, compliance, and reliability concerns associated with autonomous database management, proposing best practices for AI-driven data governance. Future research directions include the integration of quantum computing for database acceleration, AI-driven anomaly detection for enhanced cybersecurity, and the application of reinforcement learning for real-time database optimization. This paper provides a strategic roadmap for enterprises looking to adopt AI-driven autonomous database solutions to drive innovation and competitive advantage.