State-of-the-art process discovery methods construct free-choice process models from event logs.
Implementing systems based on Machine Learning to detect fraud and other Non-Technical Losses (NTL) is challenging: the data available is biased, and the algorithms currently used are black-boxes that cannot be either easily trusted or understood by stakeholders.
Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes.
The cornerstone of this approach is a technique to learn a directly follows graph called mutual fingerprint from the event logs of the two variants.
One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i. e., characterize the ability of the model to produce behavior unrelated to the one observed.
The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered.