AI-Driven Predictive Maintenance in Relational Database Systems

Abstract

In the era of data-driven decision-making, relational database systems are critical for managing and storing vast amounts of information. However, maintaining these systems efficiently is paramount to ensure high availability and performance. This paper explores the integration of Artificial Intelligence (AI) techniques in predictive maintenance strategies for relational database systems. By leveraging machine learning algorithms and data analytics, we propose a framework that analyzes historical performance metrics and error logs to predict potential failures and optimize maintenance schedules. The framework employs a multi-faceted approach, combining anomaly detection, trend analysis, and predictive modeling to identify patterns that precede system failures. Experimental results demonstrate that the AI-driven predictive maintenance approach significantly reduces downtime and maintenance costs while enhancing overall system reliability. This research contributes to the field of database management by providing a novel solution that utilizes AI to enhance the operational efficiency of relational databases.