Enhancing Database Architectures with Artificial Intelligence (AI)

Artificial intelligence and Database Management Systems Integration bring intelligence, adaptability, and independence in the world of databases. Relational database management systems structure the data and have been the foundations for implementing them, although they face several challenges that have arisen from modern-day environments of computing and information processing, such as scalability, real-time processing, the incorporation of unstructured data, and capabilities for making proactive decisions. As a result, new approaches like NoSQL and NewSQL appeared to address various and scalable needs of the applications. AI concepts such as Machine learning (ML), Deep learning (DL), and Natural language processing (NLP) have brought about improvement of advanced functions and optimization of efficiency into current database systems. These are self-tuning, query optimization, predictive caching, and natural language interfaces that enable a database to work autonomously while offering high-performance and reliability service. This paper focuses on the traditional and advanced DBMS architectures, the development and integration of AI-based DBMS, and other novelties such as federated learning and reinforcement-based cache.