AI-Driven Query Optimization: Revolutionizing Database Performance and Efficiency
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© 2024 by IJCTT Journal
Volume-72 Issue-3
Year of Publication : 2024
Authors : Vijay Panwar
DOI :  10.14445/22312803/IJCTT-V72I3P103

How to Cite?

Vijay Panwar, "AI-Driven Query Optimization: Revolutionizing Database Performance and Efficiency," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 18-26, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P103

Abstract
The advent of artificial intelligence (AI) in optimizing database queries marks a significant milestone in the realm of database management, promising to elevate performance and efficiency to unprecedented levels. Traditional query optimization techniques, while effective to a certain extent, struggle to keep pace with the complexities and dynamic nature of modern, large-scale databases. This research paper delves into the transformative potential of AI-driven query optimization, showcasing how machine learning algorithms can intelligently predict and execute the most efficient query plans based on historical and real-time data. Through detailed experimental analyses, this study compares the performance of AI-optimized queries against traditional optimization methods across various database systems. Additionally, it presents case studies highlighting the practical benefits and challenges of implementing AI-driven optimization in real-world scenarios. The paper also explores future developments in this field, including the scalability of AI techniques, the evolution towards fully autonomous self-tuning databases, and the broader application of AI in optimizing NoSQL and graph databases. The findings underscore the pivotal role of AI in enhancing database performance and efficiency, paving the way for more responsive, cost-effective, and scalable data management solutions.

Keywords
AI-driven Query Optimization, Database Performance, Machine Learning Models, Adaptive Query Optimization, Database Efficiency, Query Execution Plans, Cost Models, Performance Benchmarks, Autonomous Databases, Scalability, Dynamic Databases, Real-time Query Optimization, Data Management, Computational Overhead, Query Prediction Algorithms, Self-Tuning Systems, Cross-Platform Optimization, Data Privacy and Security, Legacy System Integration, Resource Utilization .

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