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.

Keywords: BPMN, Facade Maintenance, System Dynamics
Astract: Business process modeling and system dynamics are different approaches that are used in the design and management of organizations. Both approaches are concerned with the processes in, and around, organizations with the aim to identify, design and understand their behavior as well as potential improvements. At the same time, these approaches differ considerably in their methodological focus. While business process modeling specifically takes the (control flow of) business processes as its primary focus, system dynamics takes the analysis of complex and multi-faceted systems as its core focus. More explicitly combining both approaches has the potential to better model and analyze (by way of simulation) complex business processes, while specifically also including more relevant facets from the environment of these business processes. Furthermore, the inherent ability for simulation of system dynamics models, can be used to simulate the behavior of processes over time, while also putting business processes in a broader multi-faceted context. In this paper, we report on initial results on making such a more explicit combination of business process modeling and system dynamics. In doing so, we also provide a step-by-step guide on how to use BPMN based models and system dynamics models together to model and analyze complex business processes, while illustrating this in terms of a case study on the maintenance of building facades.

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: Textbook, Conceptual Modeling, Ontology-Guided Modeling

Keywords:

Keywords: data strategy, data management, semiotic triangle
Astract: It appears that, in an increasingly digital world, data is becoming more and more important for organizations. In several real-world cases in the Netherlands, we have seen that organizations struggle with issues such as: a) What data do we have? b) Which data do we need for value creation? c) Which strategic choices do we make around using data? d) Which strategic choices do we make around managing data as an asset? And e) Which skills do our people need in light of the previous items? The context for these organizations is different, but the challenges are the same. Our position is that the use of models can help to get to grips with the complexity of the aforementioned challenges. In this paper we explore a) the relationship between data and models, and b) a framework that connects data management as a means to care for data as an asset, and data as a means to achieve strategic objectives of the organization. In developing the latter, we follow a design science approach; we explore cases to understand challenges and requirements for such a framework as well as show how the framework helped to solve the challenges in these organizations.

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: AI, Enterprise Architecture, Enterprise Modelling
Astract: A future enterprise will be a complex ecosystem (or system of systems) that operates in a dynamic uncertain environment. It will need to continue delivering the stated goals while dealing with unforeseen changes along multiple dimensions such as events opening up new opportunities or constraining the existing ones, competitor actions, regulatory regime, law of the land and technology advance/obsolescence. Customers increasingly demand highly personalized services and user experiences that can change depending on market trends. The goals that drive the operation of an enterprise can change over time. Businesses are increasingly regulated. Given the increased dynamics, existing regulations will keep changing frequently, and new regulations will get introduced at a faster rate. Responsive compliance management with minimal exposure to risk will therefore be a universal key requirement that will be felt increasingly acutely across business domains. Increasingly, enterprises are pledging to the sustainable development goals proposed by the UN. Quite a few domains are witnessing stiff competition from new entrants such as FinTech companies in banking. Enterprises need to significantly reduce the costs to continue to be viable in the face of this technology-centric agile competition. Moreover, as the Covid-19 pandemic has revealed, enterprises need to be prepared to quickly adapt in the face of black swan events.