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.

Keywords: Evolution, Digital Twin, Sustainability, Smart Ecosystems, Model-driven Engineering

Keywords: Sustainability, Model-Driven Engineering, Microservices Architectures, Software Architecture, Optimization

Keywords: multi-modal AI, Conceptual Modeling, Comprehension, Large Language Models (LLMs)

Keywords: Domain Modeling, Large Language Model, Process Mining, UML
Astract: Large Language Models (LLMs) have opened new opportunities in modeling in general, and conceptual modeling in particular. With their advanced reasoning capabilities, accessible through natural language interfaces, LLMs enable humans to deepen their understanding of different application domains and enhance their modeling skills. However, the open-ended nature of these interfaces results in diverse interaction behaviors, which may also affect the perceived usefulness of LLM-assisted conceptual modeling. Existing works focus on various quality metrics of LLM outcomes, yet limited attention is given to how users interact with LLMs for such modeling tasks. To address this gap, we present the design and findings of an empirical study conducted with information systems students. After labeling the interactions according to their intentions (e.g., Create Model, Discuss, or Present), and representing them as an event log, we applied process mining techniques to discover process models. These models vividly capture the interaction behaviors and reveal recurrent patterns. We explored the differences in interacting with two LLMs (GPT 4.0 and Code Llama) for two modeling tasks (use case and domain modeling) across three application domains. Additionally, we analyzed user perceptions regarding the usefulness and ease of use of LLM-assisted conceptual modeling.

Keywords:

Keywords: BIM, simulation, data model, metamodel, compliance checking
Astract: Making sure that Building Information Modelling (BIM) models comply with building codes and contractual requirements is typically a time-consuming and overhead-heavy task. For this reason, there is a push for its automation. However, there are significant challenges to overcome. For example, most technical guidelines and contractual requirements are formulated in a natural language and would need to be translated into a formal representation for digital processing. Furthermore, there is no formal mechanism that enables the declaration, enforcement, and verification of compliance to those in a BIM model. In this work, we present a fundamental approach that makes the last three possible at any level of granularity, i.e., throughout the building’s entire life cycle.

Keywords: business capability, conceptual model, data valuation, Data value, reference ontology
Astract: Despite its recognition as primary asset, enterprises struggle to determine data value due to fragmented and impractical approaches. This paper develops a reference ontology for Data Valuation Business Capabilities (DVBC) leveraging the systematic approach for building ontologies, ArchiMate and integrating scientific insights with ex-ante expert interview validation. Comprising twelve groupings and 66 components, anchored in established ontologies and assessed against (non)-functional requirements, the ontology shapes the fragmented data valuation landscape into a structuring frame for enterprises. While advancing value modelling in information systems research, the ontology faces limitations like detailed process modelling deficiency, ex-post validation potential, and modelling language boundaries.