Year: 2024
Type: article
Abstract: This paper explores the integration of artificial intelligence (AI) in extensive database management systems (DBMS) to enhance data processing, retrieval, and decisionmaking. As data volumes continue to grow exponentially, traditional database management techniques struggle to maintain efficiency and accuracy. We present a framework that leverages AI technologies, Such as machine learning and natural language processing-to automatically classify data, optimize query performance, and enhance data integrity. Our method allows for adaptive learning from user interactions and patterns by implementing AI-driven algorithms. allowing for realtime adjustments and predictive analytics. Case studies demonstrate significant improvements in data accessibility, user experience, and operational efficiency. The findings suggest that AI-enhanced DBMS not only streamline data management tasks but also empower organizations to derive deeper insights from their data assets, ultimately driving informed decision-making and fostering innovation. Future research directions are proposed to further explore the scalability and security implications of AI in database management.
Authors: Karan Lande, Priyanka Bhore
Institution: Ajeenkya DY Patil University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404828299