while this structure is not discoverable from a low-level log of this process. The bottom right Petri net shows the example process at a high-level. The high-level process model allows for any finite length alternating sequence of *Taking medicine* and *Eating* activities. The *Taking medicine* high-level activity is defined as a subprocess, corresponding to the topmost Petri net, which consists of low-level events *Medicine cabinet (MC)*, *Dishes & cups cabinet (DCC)*, and *Water (W)*. The *Eating* high-level event is also defined as a subprocess, shown in the bottom left Petri net, which consists of low-level events *Dishes & cups cabinet (DCC)* and *Cutlery drawer (CD)* that can occur an arbitrary number of times in any order and low-level event *Dishwasher (D)* which occurs exactly once, but at an arbitrary point in the *Eating* process.

When we apply the Inductive Miner process discovery algorithm [@Leemans2013] to low-level traces generated by the hierarchical process of Figure \[fig:double\_flower\], we obtain the process model shown in Figure \[fig:merged\_flower\]. The obtained process model allows for almost all possible sequences over the alphabet $\{CD,D,DCC,MC,W\}$, as the only constraint introduced by the model is that *DCC* and *D* are required to be executed starting from the initial 