Keywords: Quantum Computing, Software Engineering, domain-specific modeling

Keywords: Process Mining, Multi-Modal Segmentation, Conceptual Modeling

Keywords: Model-Driven Engineering
Astract: Model-driven engineering (MDE) and Artificial Intelligence (AI) have gained momentum in recent years, and the fusion of techniques and tools in the two domains paves the way for several applications. Such integrations—which we call MDE Intelligence—are bidirectional, i.e., MDE activities can benefit from the integration of AI ideas and, in return, AI can benefit from the automation and subject-matter-expert integration offered by MDE. The 5th edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence), held in conjunction with the IEEE/ACM 26th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2023), follows up on the success of the previous four editions, and provides a forum to discuss, study, and explore the opportunities offered and the challenges raised by integrating AI and MDE.

Keywords: Data set, Enterprise architecture, Enterprise modeling, FAIR, Machine learning
Astract: The conceptual modeling community and its subdivisions of enterprise modeling are increasingly investigating the potentials of applying artificial intelligence, in particular machine learning (ML), to tasks like model creation, model analysis, and model processing. A prerequisite—and currently a limiting factor for the community—to conduct research involving ML is the scarcity of openly available models of adequate quality and quantity. With the paper at hand, we aim to tackle this limitation by introducing an EA ModelSet, i.e., a curated and FAIR repository of enterprise architecture models that can be used by the community. We report on our efforts in building this data set and elaborate on the possibilities of conducting ML-based modeling research with it. We hope this paper sparks a community effort toward the development of a FAIR, large model set that enables ML research with conceptual models.

Keywords: Deployment, Flexibility, GLSP, Language Server Protocol, LSP, Modeling tool, Tool development, Web modeling
Astract: In the past decade, the modeling community has produced many feature-rich modeling editors and tool prototypes not only for modeling standards but particularly also for many domain-specific languages. More recently, however, web-based modeling tools have started to become increasingly popular in the industry for visualizing and editing models adhering to such languages. This new generation of modeling tools is built with web technologies and offers much more flexibility when it comes to their user experience, accessibility, reuse, and deployment options. One of the technologies behind this new generation of tools is the Graphical Language Server Platform (GLSP), an open-source client-server framework hosted under the Eclipse foundation, which allows tool developers to build modern diagram editors for modeling tools that run in the browser or can be easily integrated into IDEs such as Eclipse, VS Code, or Theia. In this paper, we describe our vision for more flexible modeling tools which is based on our experiences from developing several traditional and web-based modeling tools in an industrial and academic context. With that, we aim at sparking a new line of research and innovation in the modeling community for modeling tool development practices and to explore opportunities, advantages, and limitations of web-based modeling tools, as well as bridge the gap between scientific tool prototypes and industrial tools being used in practice.