AI-Driven Schema Evolution and Management in Heterogeneous Databases

Abstract

In the era of big data, organizations often deal with heterogeneous databases that necessitate the evolution and management of schemas to accommodate changing requirements and diverse data sources. This paper explores AI-driven techniques for schema evolution and management in heterogeneous databases, focusing on automating the adaptation of schema structures to enhance data integration and querying capabilities. We present an AI framework that leverages machine learning algorithms to analyze usage patterns, data dependencies, and schema relationships. By employing predictive analytics, the framework can anticipate necessary schema modifications, ensuring that databases remain aligned with evolving application needs. Additionally, we discuss the implementation of intelligent data mapping techniques that facilitate seamless integration across different data formats and structures. Case studies illustrate the effectiveness of our proposed methods in improving performance, reducing manual intervention, and enhancing the overall flexibility of database systems. This research contributes to the field by providing a comprehensive approach to managing schema evolution in heterogeneous environments, ultimately fostering better data management practices and improved decisionmaking processes.