Machine Learning Techniques for Automated Query Optimization in Relational Databases

Abstract

Query optimization is a cornerstone of efficient database management, crucial for maintaining
performance as databases scale in size and complexity. Traditional query optimization
techniques, while effective, often rely on static rules and cost-based methods that struggle with
dynamic workloads and diverse query patterns. Machine learning (ML) offers promising
solutions to these challenges by providing adaptive, data-driven approaches that can predict and
select optimal execution plans. This article explores the application of various ML techniques,
including reinforcement learning, supervised learning, unsupervised learning, and deep learning,
in query optimization. We discuss their methodologies, advantages, and practical
implementations, supported by case studies and empirical data. Our findings highlight the
potential of ML to revolutionize query optimization, making it more efficient, scalable, and
adaptable to changing database environments.