Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities

The role of large language models (LLMs) in enterprise modeling has recently started to shift from academic research to that of industrial applications. Thereby, LLMs represent a further building block for the machine-supported generation of enterprise models. In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs in this context. In addition, the findings of an expert survey and ChatGPT-4o-based experiments demonstrate that LLM-based model generations exhibit minimal variability, yet remain constrained to specific tasks, with reliability declining for more intricate tasks. The survey results further suggest that the supervision and intervention of human modeling experts are essential to ensure the accuracy and integrity of the generated models.

Multi-Faceted Evaluation of Modeling Languages for Augmented Reality Applications -- The Case of ARWFML

The evaluation of modeling languages for augmented reality applications poses particular challenges due to the three-dimensional environment they target. The previously introduced Augmented Reality Workflow Modeling Language (ARWFML) enables the model-based creation of augmented reality scenarios without programming knowledge. Building upon the first design cycle of the language's specification, this paper presents two further design iterations for refining the language based on multi-faceted evaluations. These include a comparative evaluation of implementation options and workflow capabilities, the introduction of a 3D notation, and the development of a new 3D modeling environment. On this basis, a comprehensibility study of the language was conducted. Thereby, we show how modeling languages for augmented reality can be evolved towards a maturity level suitable for empirical evaluations.

A Bibliometric Analysis of the BPM Conference Using Computational Data Analytics

The BPM conference has a long tradition as the premier venue for publishing research on business process management. For exploring the evolution of research topics, we present the findings from a computational bibliometric analysis of the BPM conference proceedings from the past 15 years. We used the publicly available DBLP dataset as a basis for the analysis, which we enriched with data from websites and databases of the relevant publishers. In addition to a detailed analysis of the publication results, we performed a content-based analysis of over 1,200 papers from the BPM conference and its workshops using Latent Dirichlet Allocation. This offers insights into historical developments in Business Process Management research and provides the community with potential future prospects.