Measuring key performance indicators, such as queue lengths and waiting times, using event logs serve for improvement of resource-driven business processes. However, existing techniques assume the availability of complete life cycle information, including the time a case was scheduled for execution (aka arrival times). Yet, in practice, such information may be missing for a large portion of the recorded cases.
In this talk, I will propose a methodology to address missing life-cycle data by incorporating predicted information in business processes performance analysis. The approach builds upon techniques from queueing theory and leverages supervised learning to accurately predict performance indicators based on an event log with missing data. I will also present experimental results to demonstrate the effectiveness of the approach on synthetic and real-world data.