2025/02/26 by Philipp-Lorenz Glaser, Emanuel Sallinger, Dominik Bork · 1 voice · 1 citation
Decision Sciences · Business, Management and Accounting · #Scientific Computing and Data Management #Big Data and Business Intelligence #Data Quality and Management
paper · pdf · doi:10.1007/s10270-025-01278-1
openalex publication_date 2025/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Abstract Conceptual modeling research is increasingly investigating the application of artificial intelligence (AI) and machine learning (ML) to automate tasks like model creation, completion, analysis, and processing. This trend also applies to enterprise architecture (EA) research. In contrast to its neighboring disciplines, such as business process management, EA lacks proper guidelines, patterns, and best practices to create high-quality EA models. A currently limiting factor for conducting AI-based research to bridge these gaps is the scarcity of openly available models of adequate quality and quantity. With this paper, our aim is to address this limitation by introducing the extended EA ModelSet , a curated and FAIR repository of enterprise architecture models represented in the ArchiMate modeling language that can be used by the research and practitioner community. We report on our efforts to build the EA ModelSet and elaborate on exemplary future empirical and ML-based research that can facilitate the dataset. We hope that this paper sparks a community effort toward the further development and maintenance of the EA ModelSet.