Handling duplicated tasks in process discovery by refining event labels

Processes may require to execute the same activity in different stages of the process. A human modeler can express this by creating two different task nodes labeled with the same activity name (thus duplicating the task). However, as events in an event log often are labeled with the activity name, discovery algorithms that derive tasks based on labels only cannot discover models with duplicate labels rendering the results imprecise. For example, for a log where “payment” events occur at the beginning and the end of a process, a modeler would create two different “payment” tasks, whereas a discovery algorithm introduces a loop around a single “payment” task. In this paper, we present a general approach for refining labels of events based on their context in the event log as a preprocessing step. The refined log can be input for any discovery algorithm. The approach is implemented in ProM and was evaluated in a controlled setting. We were able to improve the quality of up to 42 % of the models compared to using a log with imprecise labeling using default parameters and up to 87 % using adaptive parameters. Moreover, using our refinement approach significantly increased the similarity of the discovered model to the original process with duplicate labels allowing for better rediscoverability. We also report on a case study conducted for a Dutch hospital.