An Enhancement of Information Retrieval in Digital Transformation Framework Using the Open Group Architecture Framework
Year: 2025
Type: article
Abstract: Digital transformation has significantly increased the volume and complexity of enterprise data, creating the need for information retrieval (IR) systems capable of providing accurate, context-aware, and scalable search services. While the TOGAF enterprise architecture framework provides governance for enterprise information systems, it does not define modern retrieval algorithms capable of addressing semantic search challenges. This study proposes an enhanced information retrieval framework that integrates semantic search, natural language processing (NLP), machine learning-based document ranking, edge computing, and federated learning within a TOGAF-guided enterprise architecture. The proposed framework was evaluated using an enterprise document collection containing structured and unstructured digital transformation data. Performance was compared with established information retrieval baselines using precision, recall, accuracy, F1-score, and query response time. Experimental results demonstrate that the proposed framework improves retrieval effectiveness while reducing query latency through distributed processing. Ablation analysis further illustrates the contribution of semantic retrieval, intelligent re-ranking, and distributed computing components to the overall system performance. Rather than claiming regulatory compliance, the framework is designed to align with security controls recommended by GDPR, NIST Cybersecurity Framework, and ISO/IEC 27001 through role-based access control, encryption, audit logging, and privacy-preserving federated learning. The findings demonstrate that integrating modern artificial intelligence techniques into a TOGAF-guided enterprise architecture can significantly improve enterprise information retrieval while maintaining scalability, security, and governance requirements. The proposed framework provides practical guidance for organizations implementing intelligent search systems as part of digital transformation initiatives
DOI: 10.54117/y8k6fj52
Institution: Kaduna State University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7204470491