Machine Learning Approaches for Enhancing Query Optimization in Large Databases
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More effective query optimization strategies in large-scale databases are required due to the growing volume and complexity of data in contemporary applications. Performance inefficiencies result from traditional query optimization techniques, such as rule-based and cost-based approaches, which frequently find it difficult to manage dynamic and complicated workloads. By utilizing deep learning, reinforcement learning, and predictive analytics to enhance query execution plans, indexing, and workload management, machine learning (ML) has become a game-changing method for improving query optimization. With its many advantages—including workload-aware indexing, adaptive tuning, and real-time performance improvements—ML-driven optimization approaches are especially well-suited for distributed and cloud-based database setups. However, challenges remain, such as the need for more explainable AI-powered optimizers, security vulnerabilities, and the high computational costs of training machine learning models. To ensure reliable and efficient database management, future research should focus on creating hybrid optimization frameworks, strengthening security measures, and making machine learning-based decision-making more explainable. By addressing these challenges, machine learning-powered query optimization could open the door to smarter, more flexible, and scalable database systems.
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