Advancing Database Management Through Artificial Intelligence: A Comprehensive Framework for Autonomous, Self-Optimizing Data Ecosystems.

The article focuses on advancing database management systems (DBMS) through the integration of Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), to develop autonomous, self-optimizing data ecosystems. It presents a novel AI-native DBMS architectural framework and empirically benchmarks traditional systems (MySQL 8.0, PostgreSQL 14) against AI-augmented platforms (Oracle Autonomous Database, Google AlloyDB AI, Microsoft Azure SQL with AI Insights) using standardized TPC-C and TPC-H workloads. Results demonstrate significant improvements in query latency (42.2% reduction), throughput (43.6% increase), administrative workload (38.3% reduction), anomaly detection accuracy (35.3% improvement), and unplanned downtime (55% reduction) due to AI-driven automation. The study also discusses core AI applications such as intelligent query optimization, automated tuning, predictive maintenance, anomaly detection, and natural language query interfaces, while addressing challenges related to explainability, resource costs, legacy integration, data privacy, and sustainability, outlining future research directions including explainable AI, energy-efficient models, and privacy-preserving techniques.