Keywords: Conceptual Models, Domain Models, Modeling, Return on Modeling Efforts, Risk Assessment
Astract: Modelers and organizations often struggle to assess the benefits and drawbacks of modeling activities. This paper proposes addressing this challenge through a risk-oriented lens, leveraging the Common Ontology of ValuE and Risk (COVER) and the Reference Ontology for Security Engineering (ROSE). The proposal focuses on identifying assets at risk throughout the modeling process to clarify: when models mitigate risks and contribute to cost savings (models as risk deterrents), when models introduce risk to other assets (models as risk sources), or when they are vulnerable to risk events themselves (models as assets at risk), potentially generating additional costs. This perspective enables modelers and organizations to evaluate the benefits and costs of modeling practices, aligning investments with organizational goals, while helping researchers identify gaps for enhancing modeling languages, methods, and tools. The proposal is evaluated by analyzing case studies from the literature and interviews with nine professionals and researchers.

Keywords: Process Mining

Keywords: Conceptual modeling, Customer journey map, Modeling costs, Multi-method research approach, Pain points
Astract: Conceptual models offer numerous benefits but require significant investments, requiring modelers to strive to balance costs and benefits. Understanding the modeling process and the frustrations experienced by modelers can provide valuable insights for this assessment. While research acknowledges certain instances of modelers’ dissatisfaction, its scope often limits detailed examination. This study seeks to identify and analyze the main pain points associated with domain modeling through a five-phase empirical study using a multi-method approach. We identified 71 pain points, synthesized them to 41, and prioritized 16 as the most significant and prevalent in domain modeling. We then refined, documented, and exemplified the prioritized pain points, analyzed their potential causes, and discussed their practical implications. Our findings provide valuable insights for improving modelers’ experiences and optimizing the modeling process.

Keywords: Business capability, Data valuation, Data value, Taxonomy
Astract: In our data-centric society, the imperative to determine the value of data has risen. Therefore, this paper presents a taxonomy for a data valuation business capability. Utilizing an initial taxonomy version, which originated from a systematic literature review, this paper validates and extends the taxonomy, culminating in four layers, twelve dimensions, and 59 characteristics. The taxonomy validation was accomplished by conducting semi-structured expert interviews with eleven subject matter experts, followed by a cluster analysis of the interviews, leading to a taxonomy heatmap including practical extensions. This paper's implications are manifold. Firstly, the taxonomy promotes a common understanding of data valuation within an enterprise. Secondly, the taxonomy aids in categorizing, assessing, and optimizing data valuation endeavors. Thirdly, it lays the groundwork for potential data valuation standards and toolkits. Lastly, it strengthens theoretical assumptions by grounding them in practical insights and offers an interdisciplinary research agenda following the taxonomy dimensions and characteristics.

Keywords: Domain Modeling, Large Language Models, Tree of thoughts
Astract: Domain modeling is typically an iterative process where modeling experts interact with domain experts to complete and refine the model. Recently, we have seen several attempts to assist, or even replace, the modeler with a Large Language Model (LLM). Several LLM prompting strategies have been attempted, but with limited success. In this paper, we advocate for the adoption of a Tree-of-Thoughts (ToT) strategy to overcome the limitations of current approaches based on simpler prompting strategies. With a ToT strategy, we can decompose the modeling process into several sub-steps using for each step a specialized set of generators and evaluators prompts to optimize the quality of the LLM output. As part of our adaptation, we provide a Domain-Specific Language (DSL) to facilitate the formalization of the ToT process for domain modeling. Our approach is implemented as part of an open source tool available on GitHub.