Keywords: Architecture, Domain model, Enterprise architecture, Enterprise architecture model, Enterprise architecture modeling, Model quality, Ontology
Astract: Models have long since been used, in different shapes and forms, to understand, communicate about, and (re)shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This is also the case in the context of enterprise architecture (EA), where we see a wide range of models in many different shapes and forms being used as well. Researchers in EA modeling usually introduce their own lexicon, and perspective of what a model actually is, while accepting (often implicitly) the accompanying ontological commitments. Similarly, practitioners of EA modeling implicitly also commit to (different) ontologies, resulting in models that have an uncertain ontological standing. This is because, for the subject domain of enterprise architecture models (as opposed to the content of such models), no single ontology has gained major traction. As a result, studies into aspects of enterprise architecture models, such as “model quality” and “return on modeling effort”, are fragmented, and cannot readily be compared or combined. This paper proposes a comprehensive applied ontology, specifically geared to enterprise architecture modeling. Ontologies represent structured knowledge about a particular subject domain. It allows for study into, and reasoning about, that subject domain. Our ontology is derived from a theory of modeling, while clarifying concepts such as “enterprise architecture model”, and introduces novel concepts such as “model audience” and “model objective”. Furthermore, the relevant interrelations between these different concepts are identified and defined. The resulting ontology for enterprise architecture models is represented in OntoUML, and shown to be consistent with the foundational ontology for modeling, Unified Foundational Ontology.

Keywords: Circular Systems Engineering, Degrowth and IT, Digital Twins, Digitalization, Ethics, Information Systems Engineering, Model-Based Systems Engineering, Model-Driven Engineering, Modeling, Sustainability, Systems Engineering
Astract: Sustainability should become a key concern in the next generation of engineered systems. While this expectation is relatively straightforward, the question of how to get there is less obvious. The multi-dimensional and intricate nature of sustainability poses challenges in designing sustainable systems and analyzing sustainability properties. Finding trade-offs between economic, environmental, societal, and technological aspects of sustainability is a wicked problem and calls for advanced modeling and simulation methods. In this paper, we report on a panel discussion held at the 28th Working Conference on Exploring Modeling Methods for Systems Analysis and Development (EMMSAD) with four esteemed experts representing four complementary and often conflicting perspectives on the role of modeling for sustainability – stakeholders, digitalization, degrowth and IT, and ethics. We report the key arguments of the panelists, discuss the roles of modeling in the analysis and design of sustainable systems, and, finally, elaborate on the conflicts among the perspectives, their effects, and potential resolutions.

Keywords: multi-modal AI, Conceptual Modeling, Comprehension, Large Language Models (LLMs)

Keywords: business capability, conceptual model, data valuation, Data value, reference ontology
Astract: Despite its recognition as primary asset, enterprises struggle to determine data value due to fragmented and impractical approaches. This paper develops a reference ontology for Data Valuation Business Capabilities (DVBC) leveraging the systematic approach for building ontologies, ArchiMate and integrating scientific insights with ex-ante expert interview validation. Comprising twelve groupings and 66 components, anchored in established ontologies and assessed against (non)-functional requirements, the ontology shapes the fragmented data valuation landscape into a structuring frame for enterprises. While advancing value modelling in information systems research, the ontology faces limitations like detailed process modelling deficiency, ex-post validation potential, and modelling language boundaries.

Keywords: domain models
Astract: Humanity has long since used models in different shapes and forms to understand, redesign, communicate about, and shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This has resulted in a wide range of modeling practices. When the models as used in such modeling practices have a key role to play in the activities in which these modeling practices are ‘embedded’, the need emerges to consider the effectiveness and efficiency of such processes, and speak about modeling capabilities. In the latter situation, it becomes relevant to develop a thorough understanding of the artifacts involved in the modeling practices/capabilities. One field in which models play (an increasingly) important role is the field of system development (including software engineering, information systems engineering, and enterprise design management). In this context, we come across notions, such as views, diagrams, programs, animations, specifications, etc. The aim of this paper is to take a fundamental look at these notions. In doing so, we will argue that these notions should actually be seen as specific kinds of models, albeit for fundamentally different purposes.