marking to end up with the same marking. Firing of all other transitions in the model can be skipped. Behaviorally this model is very close to the so called “flower” model [@Aalst2011], the model that allows for all behavior over its alphabet. The alternating structure between *Taking medicine* and *Eating* that was present in the high-level process in Figure \[fig:double\_flower\] cannot be observed in the process model in Figure \[fig:merged\_flower\]. This is caused by high variance in start and end events of the high-level event subprocesses of *Taking medicine* and *Eating* as well as by the overlap in event types between these two subprocesses.

![image](Capture_IM_Red_read){width="75.00000%"}

When the event log would have consisted of the high-level *Eating* and *Taking medicine* events, process discovery techniques have no problems to discover the alternating structure in the bottom right Petri net of Figure \[fig:double\_flower\]. To discover the high-level alternating structure from a low-level event log it is necessary to first abstract the events in the event log. Through supervised learning techniques the mapping from low-level events to high-level events can be learned from examples, without requiring a hand-made ontology. Similar approaches have been explored in activity recognition in the field of ubiquitous computing, where 