Discovery and Validation of Queueing Networks in Scheduled Processes

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today’s economies. Conceptual models of such service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. Automatic mining of such operational models becomes feasible in the presence of event-data traces. In this work, we target the mining of models that assume a resource-driven perspective and focus on queueing effects. We propose a solution for the discovery and validation problem of scheduled service processes processes with a predefined schedule for the execution of activities. Our prime example for such processes are complex outpatient treatments that follow prior appointments. Given a process schedule and data recorded during process execution, we show how to discover Fork/Join networks, a specific class of queueing networks, and how to assess their operational validity. We evaluate our approach with a real-world dataset comprising clinical pathways of outpatient clinics, recorded by a real-time location system (RTLS). We demonstrate the value of the approach by identifying and explaining operational bottlenecks.