Generating non block-structured models and corresponding logs

For experimenting with process discovery and Petri nets, scientists often rely on experiments with artificial models and logs. More often than not, these models are block structured as it is easy to generate such models by simply building a random process tree and translating that into a Petri net. However, Petri nets allow for more intricate structures which may heavily affect the experimental results, i.e. the representational bias of block structured models it too limited for state-of-the-art process mining technology.

We are therefore looking for a tool to generate sound, but not block structured, Petri nets and corresponding event logs with various characteristics in terms of deviations and decision point behaviour.

Contact prof. Boudewijn van Dongen

Leave a Reply