AI-Augmented Database Management Systems for Real-Time Data Analytics

Abstract

AI-Augmented Database Management Systems (DBMS) represent a transformative approach to real-time data analytics by leveraging artificial intelligence to enhance system efficiency, query optimization, and decision-making. In this paper, we explore the integration of AI-driven algorithms within modern DBMS architectures to improve data retrieval speeds, dynamic workload management, and predictive analytics capabilities. By automating routine tasks like indexing, partitioning, and query execution plans, AI-augmented DBMSs offer improved system adaptability and performance, particularly in environments dealing with large-scale, heterogeneous data. We also highlight the use of machine learning models for anomaly detection and performance tuning, which ensures continuous system optimization. The proposed AIenhanced framework demonstrates significant improvements in query processing times and overall system throughput, making it suitable for applications that require fast and accurate real-time insights.