Process Mining and the Black Swan: An Empirical Analysis of the Influence of Unobserved Behavior on the Quality of Mined Process Models

Jana-Rebecca Rehse, Peter Fettke, Peter Loos

In: Matthias Weidlich , Ernest Teniente (editor). Proceedings of the 13th International Workshop on Business Process Intelligence. International Workshop on Business Process Intelligence (BPI-2017) 13th located at International Conference on Business Process Management September 10-14 Barcelona Spain Springer 2017.


In this paper, we present the epistomological problem of induction, illustrated by the metaphor of the black swan, and its relevance for Process Mining. The quality of mined models is typically measured in terms of four dimensions, namely fitness, precision, simplicity, and generalization. Both precision and generalization rely on the definition of ``unobserved behavior'', i.e. traces not contained in the log. This paper is intended to analyze the influence of unobserved behavior, the potential black swan, has on the quality of mined models. We conduct an empirical analysis to investigate the relation between a system, its observed and unobserved behavior and the mined models. The results show that the unobserved behavior, mainly determined by the nature of the unknown system, can have a significant impact on the quality assessment of mined models, hence eliciting the need to explicate and discuss the assumptions underlying the notions of unobserved behavior in more depth.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz