The analysis of inertial measurement unit (IMU)-based data allows tracking human behavior, detecting anomalies, and predicting human activity changes. As IMU-based data is unstructured and continuous, the application of process mining could provide additional insights into the underlying human performance. Therefore, the data has to be efficiently pre-processed in order to be used by process mining algorithms. This paper presents an approach to convert IMU-based data into structured event data for process mining. Particularly, the approach relies on methods for time-series segmentation and convolutional neural networks. In this way, activities of daily living can be identified from the unstructured data. The evaluation results show that convolutional neural networks are suitable for discovering activities when window sizes are previously known and have low cutoff values. The combination with a fixed sliding window approach for unknown window sizes appears superior.

When declarative process models (DPMs) contain contradictory constraints, they immediately become unsatisfiable. Recognizing the challenges of automated inconsistency resolution approaches in real-world scenarios, we present an interactive and comprehensive approach for resolving inconsistencies in DPMs. Our approach targets both design-time and run-time inconsistencies through incremental resolution of inconsistency cores. To guide users effectively and lower the mental effort required during the resolution process, we rank inconsistency cores based on customizable complexity measures. We also consider user preferences and familiarity with different inconsistency characteristics. Additionally, we dynamically integrate prior user decisions and resolution operations to ensure an efficient and user-centric approach. By presenting users with one inconsistency core at a time and providing decision support, our approach aims to minimize cognitive overload. Furthermore, it offers flexibility and adaptability by allowing users to apply their preferred resolution operations, reflecting the complexity of real-world scenarios.