AI-Powered Self-Healing Databases: Automated Detection and Mitigation of Database Anomalies

Abstract

In today's data-driven world, maintaining database integrity and performance is crucial for businesses and organizations. Traditional database management systems often struggle with anomaly detection and self-healing due to their static nature and lack of adaptive learning capabilities. This paper explores the integration of Artificial Intelligence (AI) into database management systems to develop self-healing databases that can automatically detect and mitigate anomalies. We review current AI techniques applied to database systems, present a framework for self-healing databases, and discuss the challenges and future directions in this emerging field.