Advancing Database Management Through Artificial Intelligence: A Comprehensive Framework for Autonomous, Self-Optimizing Data Ecosystems
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Advancing Database Management Through Artificial Intelligence: A Comprehensive Framework for Autonomous, Self-Optimizing Data Ecosystems
A. Purushotham Reddy
Independent Researcher
Latest2all, Bangalore, India
https://www.linkedin.com/in/anthem-purushotham-reddy
Email: reddyapuru@gmail.com
Abstract
The exponential growth in data complexity, velocity, and scale, projected to exceed 181 zettabytes by 2025, has fundamentally exposed the limitations of traditional Database Management Systems (DBMS). These legacy systems, reliant on manual tuning, heuristic-based optimization, and static security models, are increasingly inadequate for dynamic, heterogeneous, and high-velocity environments. This research paper presents a systematic investigation into the integration of Artificial Intelligence (AI), encompassing Machine Learning (ML) and Deep Learning (DL), to architect next-generation, self-driving DBMS. We propose a novel architectural framework for AI-native databases and employ a robust mixed-methods approach combining rigorous comparative experiments, simulation modeling, and in-depth industry case studies. Traditional systems (MySQL 8.0, PostgreSQL 14) are benchmarked against state-of-the-art AI-augmented platforms (Oracle Autonomous Database, Google AlloyDB AI, Microsoft Azure SQL with AI Insights) using standardized TPC-C and TPC-H benchmarks. Key evaluation metrics include query latency, throughput, administrative overhead, anomaly detection accuracy, and system recovery time. Our empirical results demonstrate that AI-driven automation yields a 42.2% reduction in query latency, a 38.3% decrease in administrative workload, a 35.3% improvement in anomaly detection accuracy, and a 55% reduction in unplanned downtime through predictive maintenance. These findings underscore a paradigm shift from static, reactive DBMS to adaptive, self-optimizing, and proactive data ecosystems. The paper concludes by critically examining the challenges of explainability, resource costs, and ethical governance, while outlining future research directions for explainable AI (XAI), energy-efficient models, and federated learning to ensure sustainable and trustworthy deployment.
Keywords: Artificial Intelligence, Autonomous Databases, Machine Learning, Query Optimization, Predictive Maintenance, Anomaly Detection, Explainable AI, Database Architecture.
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