This post is by Marco Pegoraro, Scientific Assistant in the Process And Data Science group at RWTH Aachen University. Contact him via email for further inquiries.
Let’s consider the analysis of a specific class of event logs: the logs that contain uncertain event data. Uncertain events are recordings of executions of specific activities in a process which are enclosed with an indication of uncertainty in the event attributes. Specifically, let’s consider the case where the attributes of an event are not recorded as a precise value but as a range or a set of alternatives. In the case of the common control-flow attributes of an event log, the case id and activity are represented by a set of alternatives, and the timestamp as a range.
We can think of this uncertain trace as a set of “classic” traces obtained by every possible choices of the values for the attributes; this is called the realization set.
In the paper “Mining Uncertain Event Data in Process Mining“, published on the first International Conference on Process Mining (ICPM), we laid down a taxonomy of possible kinds of uncertainty; we also explore an example of application, i.e. the computation of upper and lower bound for conformance cost of an uncertain trace against a reference process model. This is obtained by modeling a Petri net able to reproduce the behavior of an uncertain trace; this enables to compute the conformance exploiting a model-to-model comparison.