Abstract
High-availability database systems are critical components in modern IT infrastructure, demanding robust mechanisms for ensuring continuous operation and data integrity. This article explores integrating artificial intelligence (AI) techniques into anomaly detection processes for such systems, addressing the limitations of traditional rule-based and statistical methods. We present a comprehensive analysis of machine learning and deep learning approaches, including supervised and unsupervised learning models, autoencoders, recurrent neural networks, and hybrid solutions that combine AI with conventional techniques. The article examines the challenges of implementing AI-powered anomaly detection in high-availability environments, such as scalability, real-time processing, and the balance between sensitivity and specificity. Through case studies in the financial and e-commerce sectors, we demonstrate these advanced detection methods' practical applications and benefits. Our findings indicate that AI-driven approaches significantly enhance the accuracy and efficiency of anomaly detection, leading to improved system reliability and performance. The article concludes by discussing emerging trends, including edge computing and explainable AI, and their potential impact on the future of database management and anomaly detection.
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