Machine Learning Techniques for Automated Query Optimization in Relational Databases

Authors

  • Md Majadul Islam Jim, Mahmudul Hasan , Rebeka Sultana Graduate Student, Management Information Systems, College of Business, Lamar University, Texas, USA Author
  • Md Mahfuzur Rahman Graduate Student, Computer and Information Science, Southern Arkansas University, Arkansas, USA Author

Keywords:

Query Optimization, ML, Relational Database, Cost Estimation, Database Management Systems, Query Execution

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. 

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Published

2024-01-28

How to Cite

Machine Learning Techniques for Automated Query Optimization in Relational Databases. (2024). International Journal of Advanced Engineering Technologies and Innovations, 1(3), 514-529. http://ijaeti.com/index.php/Journal/article/view/407