Intelligent Database Migration Using DB Genie: A Machine Learning-Driven Approach

The rapid evolution of Artificial Intelligence (AI) has transformed data management in modern databases, offering innovative solutions to longstanding challenges. This thesis explores AI’s role in enhancing data organization, storage, retrieval, and analysis. Traditional database systems face limitations in handling the growing volume, velocity, and variety of data, often resulting in performance and scalability issues. AI techniques—such as machine learning, natural language processing, and neural networks—address these problems by automating complex processes, optimizing query execution, and enabling predictive analytics. This study investigates AI’s impact on data indexing, query optimization, and real-time processing, which reduces latency and enhances system responsiveness. It also examines AI applications in data cleaning and deduplication, which contribute to improved data quality and consistency. By integrating AI into database operations, organizations can gain actionable insights from streaming data and support data-driven decision-making. Moreover, the thesis considers ethical and privacy concerns in AI-based data systems, emphasizing the importance of robust governance and transparency. Through theoretical insights and practical case studies, this research shows that AI significantly boosts operational efficiency and unlocks new possibilities in big data environments. The synergy between AI and database technologies is essential for the future of scalable, intelligent data management.