Automated Data Transformation Using Intelligent Rule-Based Systems

Abstract

This paper explores automated data transformation using intelligent rule-based systems to address the limitations of traditional ETL processes, which are often manual, rigid, and error-prone. The proposed approach integrates rule-based reasoning with metadata-driven transformation to handle complex data from heterogeneous sources such as structured, semi-structured, and unstructured data. The system features a modular architecture including data ingestion, rule definition, execution, and validation. It applies condition–action rules for tasks like normalization, filtering, aggregation, and enrichment, along with a feedback mechanism for continuous improvement. Experimental results show that the approach significantly reduces transformation time while maintaining accuracy and improving data quality. Challenges such as rule conflicts, scalability, and legacy integration are also addressed through strategies like rule prioritization and hybrid architectures. The study concludes that intelligent rule-based systems offer a scalable and efficient solution for modern data transformation in big data and real-time environments.