AI-Assisted Decision-Making in Database Normalization and Optimization

Abstract

The increasing complexity of database systems and the demand for efficient data management have prompted the adoption of AI-assisted techniques in database normalization and optimization. This paper explores how artificial intelligence enhances traditional normalization processes, ensuring more efficient data structuring while maintaining data integrity and minimizing redundancy. Through AI-driven algorithms, the optimization of query performance, indexing strategies, and schema design becomes more dynamic and adaptable to real-time changes in data workloads. The integration of AI also allows for the automated detection of anomalies and inconsistencies in database structures, streamlining the decision-making process for database administrators. This study demonstrates the application of AI in transforming database management practices, ultimately improving overall performance, scalability, and decisionmaking accuracy in database environments.