Prescriptive Process Analytics is a core field in business process management that provides automated activity recommendations to improve Key Performance Indicators in business processes. Because such recommendations can directly affect individuals …
Process analytics has become predominantly data-driven: deep sequence models such as LSTMs and Transformers now dominate predictive and prescriptive process monitoring and deliver strong average-case accuracy. These models are, however, opaque, …
Given a running process case, prescriptive process analytics (PrPA) recommends the next activity that best improves a desired outcome, such as reducing cycle time. Recommendations must also be operationally feasible: an activity that improves the …
Business processes in customer support often remain undocumented, with critical decisions such as ticket escalation relying on tacit knowledge rather than explicit process models. Automating such process decisions is challenging because the …
Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical …
Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without …
Decomposition is widely used in process mining to enhance scalability, facilitate focused analysis, and mitigate the cognitive complexity of large-scale models. While both process discovery and conformance checking benefit from decomposition, …
Predictive modeling on sequential event data is critical for fraud detection and healthcare monitoring. Existing data-driven approaches learn correlations from historical data but fail to incorporate domain-specific sequential constraints and logical …
Complex processes can be difficult to understand from event data. Traditional process models effectively visualize control flows, but lack temporal context for performance analysis. To address this, the Performance Spectrum was introduced to map the …
Large Reasoning Models (LRMs), a subset of Large Language Models (LLMs) trained to articulate their chain-of-thought, have shown promise in tackling complex scientific tasks. However, evaluating and configuring their reasoning processes remains …