Keywords: Graph Neural Networks, Ontology-Driven Conceptual Models, Pre-trained Language Model, Representation Learning
Astract: Conceptual models (CMs) offer a structured way to organize and communicate information in information systems. However, current models lack adequate semantics of the terminology of the underlying domain model, leading to inconsistent interpretations and uses of information. Ontology-driven conceptual modeling languages provide primitives for articulating these domain notions based on the ontological categories, i.e., stereotypes put forth by upper-level (or foundational) ontologies. Existing CMs have been created using ontologically-neutral languages (e.g., UML, ER). Enriching these models with ontological categories can better support model evaluation, meaning negotiation, semantic interoperability, and complexity management. However, manual stereotyping is prohibitive, given the sheer size of the legacy base of ontologically-neutral models. In this paper, we present a graph language modeling framework for conceptual models that combines finetuning pre-trained language models to learn the vector representation of OntoUML models’ data and then perform a graph neural networks-based node classification that exploits the model’s graph structure to predict the stereotype of model classes and relations. We show with an extensive comparative evaluation that our approach significantly outperforms existing stereotype prediction approaches.

Keywords: autonomy, digital transformation, information value
Astract: High autonomy is challenging to achieve in digital twins. This is due to the lack of understanding of the socio-technical challenges and the information needs of digital twin autonomy. In this paper, we contextualize digital twin autonomy in terms of human and technical factors, identify novel socio-technical classes of digital twins with varying levels of autonomy, and define strategies that help improve autonomy across these classes. Our strategies are governed by information valuation models we developed specifically for digital twins. Our approach fosters a systematic top-down technique to improve the autonomy of digital twins.

Keywords: Accessibility, Conceptual Modeling, Disability, Modeling tools

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: Agility, Artificial Intelligence, Business Process Management, Large Language Model, Social Media
Astract: Business Processes enable collaboration among various stakeholders, allowing different groups (people, organizations) to work together to achieve common goals. Therefore, optimizing Business Process Management (BPM) is essential for organizational success in today’s dynamic business environment. However, traditional BPM methods often struggle in volatile execution environments characterized by rapid change, dynamic customer demands, and evolving market trends. Innovative strategies are needed to enhance BPM practices and increase the agility of collaborative business processes. To this end, a particularly promising approach is to use Large Language Models (LLM) agents (Artificial Intelligence conversational agents). These AI conversational agents can be integrated into a social media platform to ease the stakeholders’ collaboration by supporting the co-construction, design, modification, execution, and monitoring of collaborative business processes. AI conversational agents in social media platforms democratize BPM by facilitating collaborative process design and execution, streamlining interactions, and fostering seamless communication and personalized assistance, thus enhancing agility.