Year: 2025
Type: article
Abstract: As data volumes and query complexities grow in modern applications, ensuring optimal database performance has become increasingly challenging. Traditional manual tuning approaches are reactive, time-consuming, and often lack adaptability to dynamic workloads. This paper explores the integration of Artificial Intelligence (AI) and predictive analytics into database management systems (DBMS) for proactive performance tuning. By leveraging machine learning models, such as regression analysis and anomaly detection, AI-powered systems can forecast performance degradation, recommend tuning actions, and optimize resource allocation in real time. The study reviews state-of-the-art techniques in AI-driven query optimization, index selection, and workload prediction. Experimental insights demonstrate significant improvements in query execution time, throughput, and overall system responsiveness. This paper concludes that predictive analytics not only enhances DBMS efficiency but also paves the way for autonomous database tuning in cloud and enterprise environments.
Authors: Shahwan Younis Ali, Hajar Maseeh
Institution: Akre University for Applied Sciences
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411029561