Discovering hybrid process models with bounds on time and complexity

Discovering process models from event data is a highly relevant, but also a notoriously difficult, problem. Therefore, it is unsurprising that the biggest share of process mining research is devoted to process discovery. While techniques reported in scientific literature tend to produce process models that are formal, i.e., which mathematically describe the possible behaviors, commercial process mining tools return informal models (merely a “picture” not allowing for any form of formal reasoning). Hybrid process models aim at combining the best of both worlds: they capture behavior that is strongly supported by data and that can be used for formal reasoning, as well as behavior that cannot be represented in clear-cut process constructs or that does not have enough evidence in the data. This paper presents an approach for discovering hybrid Petri nets, which, unlike existing techniques, produces models that have both formal and semi-formal constructs so that even if the behavior in the data is noisy and irregular or it does not fit predefined constructs, causal relationships are still captured. Our evaluation demonstrates the advantages of combining such “deliberate vagueness” with formal guarantees. The ideas presented here are fairly general, and can serve as a foundation for other, new hybrid discovery techniques.