Keywords: design science, framework, maturity assessment, MBSE adoption
Astract: As engineering systems become increasingly complex, organizations must adopt strategic approaches to manage the interdependencies of their processes, tools, and teams. Model-Based Systems Engineering (MBSE) offers a promising solution, but transitioning from a traditional SE approach to MBSE is a complex endeavor that requires significant organizational change. This paper addresses the need for structured guidance in this process by proposing a maturity assessment framework that supports organizations in navigating this transition. The proposed framework is developed using a design science based approach and identifies key challenges, pitfalls, and best practices that are organized into several maturity levels of MBSE adoption. This structured, high-level approach provides organizations with the tools to understand their current maturity level, prioritize efforts, and avoid common missteps. The framework allows organizations to tailor the insights to their unique context, ensuring practical applicability. It emphasizes the importance of leadership, cultural readiness, technical tools, workforce development, and modeling practices for successful MBSE implementation.

Keywords: Process Mining

Keywords: GLSP, Langium, LSP, Modeling tools, UML., Web modeling
Astract: The move towards web technologies has arrived at modeling and especially modeling tools. Several frameworks and platforms have been proposed recently, aiming at the efficient realization of web-based modeling tools, i.e., tools developed with web technologies that natively allow for cloud-based deployment and use in web browsers. Most of these frameworks separate modeling tool functionality like editing, rendering, and management of the model into separate specialized components, often realized in different technologies. For the model management of Ecore-based modeling languages like the UML, the Eclipse Modeling Framework (EMF) is still prevalent. While this may maximize reuse, it comes at a cost: EMF is Java-based, entailing a polyglot technology stack, which complicates the development and hinders an entirely browser-based deployment without any backend. In this paper, we address this problem by introducing a Typescript-only approach for generating Langium-based modeling language grammars and model management servers. Using our approach, developers can build next-generation web-based modeling tools with a homogeneous technology stack, which can be executed as cloud applications or plain browser applications without any backend, if needed. We evaluate our approach by applying it to two distinct modeling tools: a Workflow and a UML modeling tool.

Keywords: ArchiMate, Artificial intelligence, Conceptual modeling, Dataset, Enterprise architecture, Enterprise modeling, FAIR, Machine learning
Astract: 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.

Keywords: Conceptual modeling, Data modeling, ER, Genetic algorithms, Graph neural networks, Model Modularization
Astract: Due to the crucial role conceptual models play in explicitly representing a subject domain, it is imperative that they are comprehensible and maintainable by humans. Modularization, i.e., decomposing an overarching, monolith model into smaller modules, is an established technique to make the model comprehensible and maintainable. Genetic Algorithms (GA) have been applied to modularize conceptual models by formulating desired structural model characteristics as multiple objectives. Recently, Graph Neural Networks (GNN)-based methods have shown promising performance in graph processing tasks, including graph clustering but outside the conceptual modeling domain. In this paper, we present a novel approach for GNN-based conceptual model modularization and comparatively analyze our approach against an existing multi-objective GA-based one. Furthermore, we provide a comparative analysis of our novel GNN model against two existing GNN-based graph clustering approaches. We investigate the dependence of the quality of the modularized solutions on the model size. We discuss the comparative results of our novel GNN-based approach and the existing GA-based approach to derive future research lines. Furthermore, our results show, that our proposed GNN-based modularization outperforms the existing GNN-based graph clustering approaches and provides a suitable alternative compared to the GA-based modularization.