Report on Current Developments in Temporal Conceptual Data Modelling and Business Process Analysis

General Direction of the Field

The field of temporal conceptual data modelling and business process analysis is experiencing a significant shift towards enhancing the expressiveness, usability, and complexity management of models. Recent developments focus on creating more intuitive and effective modelling languages that can be readily adopted by domain experts. This includes the integration of temporal constraints into conceptual data models to better support business process modelling, as well as the exploration of formal properties and complexity measures to ensure models are both accurate and manageable.

Innovations in graph neural networks (GNNs) for temporal settings are also advancing the field, particularly through the analysis of temporal message-passing mechanisms. These mechanisms are being rigorously evaluated for their expressive power, which is crucial for understanding their applicability in various temporal graph scenarios.

Additionally, there is a growing emphasis on the explainability and flexibility of categorization methods in business processes, leveraging formal concept analysis and Dempster-Shafer theory. This approach not only enhances the categorization process but also provides mechanisms for deliberation and aggregation of different perspectives, which is vital for complex organizational decision-making.

Lastly, the integration of data-awareness into business process simulation models is a notable advancement. This approach ensures that simulations are more reflective of real-world scenarios by considering the impact of data attributes on process execution, thereby improving the accuracy and reliability of predictive models.

Noteworthy Papers

• Temporal Conceptual Data Modelling Language TREND: Introduces a highly expressive language tested for understandability and usability, highlighting the importance of controlled natural language in improving model quality.
• Expressive Power of Temporal Message Passing: Provides a formal analysis of the expressive power of temporal message-passing mechanisms in GNNs, with practical implications supported by experimental evidence.
• Flexible Categorization Using Formal Concept Analysis and Dempster-Shafer Theory: Develops a framework for explainable categorization of business processes, enhancing the transparency and flexibility of categorization methods.
• Discovery and Simulation of Data-Aware Business Processes: Pioneers a data-aware approach to business process simulation, significantly improving the accuracy of predictive models by integrating data attributes into the simulation process.