Keywords:

Keywords: Generic Framework, Genetic Algorithm, Modularization
Astract: Conceptual models need to be comprehensible and maintainable by humans to exploit their full value in faithfully representing a subject domain. Modularization, i.e. breaking down the monolithic model into smaller, comprehensible chunks has proven very valuable to maintain this value even for very large models. The quality of modularization however often depends on application-specific requirements, the domain, and the modeling language. A well-defined generic modularizing framework applicable to different modeling languages and requirements is lacking. In this paper, we present a customizable and generic multi-objective conceptual models modularization framework. The multi-objective aspect supports addressing heterogeneous requirements while the framework’s genericity supports modularization for arbitrary modeling languages and its customizability is provided by adopting the modularization configuration up to the level of using user-defined heuristics. Our approach applies genetic algorithms to search for a set of optimal solutions. In this paper, we present the details of our Generic Genetic Modularization Framework with a case study to show i) the feasibility of our approach by modularizing models from multiple modeling languages, ii) the customizability by using different objectives for the modularization quality, and, finally, iii) a comparative performance evaluation of our approach on a dataset of ER and ECore models.

Keywords: Conceptual Modeling, Artificial Intelligence, Systematic Mapping Study
Astract: In conceptual modeling (CM), humans apply abstraction to represent excerpts of reality for means of understanding and communication, and processing by machines. Artificial Intelligence (AI) is applied to vast amounts of data to automatically identify patterns or classify entities. While CM produces comprehensible and explicit knowledge representations, the outcome of AI algorithms often lacks these qualities while being able to extract knowledge from large and unstructured representations. Recently, a trend toward intertwining CM and AI emerged. This systematic mapping study shows how this interdisciplinary research field is structured, which mutual benefits are gained by the intertwining, and future research directions.

Keywords: Artificial intelligence, DMN, Enterprise modeling, Explainable AI, Machine learning, Model-driven engineering
Astract: The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.

Keywords: Archi, ArchiMate, Enterprise architecture, Knowledge graph, Modeling tool
Astract: This paper presents the EAKG Toolkit that entails a new Knowledge Graph-based representation of enterprise architecture (EA) models and further enables reasoning on EA knowledge. Our developed EAKG Toolkit is unique in the sense that it i) transforms ArchiMate models into a KG representation – the Enterprise Architecture Knowledge Graph (EAKG), ii) visualizes the EAKG for interactive exploration, and iii) extends the EAKG with additional nodes and edges to visually represent detected EA smells.