Keywords: Process Models, Transformer models, Multimodal Evidence, Process Representation
Astract: Stakeholders can struggle to understand and engage with process models due to a mismatch between the technical language used and their own domain-specific jargon and personal communication styles. The paper explores the application of transformer-based architectures to enhance the representation of process models and additional multimodal process data by tailoring them to the language of stakeholders. We present an approach that personalizes process model representations through two types of paraphrasers: one that aligns with domain-specific jargon and another that adapts to individual stakeholder styles. We developed a golden dataset from process model-stakeholder interaction simulation and a silver dataset using large language models to train and validate our approach. Initial findings suggest that these methods could enhance stakeholder engagement and contribute to better teaching of process mining and procedural thinking.

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.