A Framework for Automated Data Integration in Advanced Information Systems

The increasing complexity of advanced information systems has created significant challenges in integrating, managing, and exchanging data across heterogeneous technological environments. Modern organizations rely on multiple interconnected systems, including Product Lifecycle Management (PLM), Model-Based Systems Engineering (MBSE), Enterprise Resource Planning (ERP), digital twins, cloud platforms, and artificial intelligence (AI)-enabled applications. However, differences in data structures, communication protocols, semantic representations, and lifecycle requirements frequently result in fragmented information environments. These challenges can affect data consistency, interoperability, operational efficiency, and decision-making. This study proposes an integrated framework for automated data integration in advanced information systems. The proposed framework combines semantic data mapping, standardized information exchange, metadata management, knowledge graphs, artificial intelligence, and lifecycle-oriented data governance. It aims to establish a structured approach through which heterogeneous information systems can exchange, interpret, validate, and synchronize information with reduced manual intervention. The framework consists of six interconnected layers: data acquisition, data standardization, semantic integration, intelligent processing, lifecycle synchronization, and governance and monitoring. Particular attention is given to interoperability between PLM and MBSE environments and the integration of digital twin technologies. A conceptual design-oriented research methodology is adopted to identify integration challenges, examine relevant standards and engineering approaches, and develop an architecture addressing the identified requirements. The proposed framework provides a foundation for improving information consistency, supporting traceability, and enabling coordinated decision-making throughout complex system lifecycles. Rather than presenting unverified experimental performance figures, the study establishes architectural propositions and evaluation criteria for future implementation. The findings suggest that automated data integration should be treated as an organizational and lifecycle management capability rather than merely a technical data-transfer activity.