Mining processes with multi-instantiation

Process mining, in particular discovering process models by mining event traces, is becoming a widely adopted practice. However, when the underlying process contains subprocesses which are instantiated multiple times in parallel, classical process mining techniques that assume a flat process are not directly applicable. Their application can cause one of two problems: either the mined model is overly general, allowing arbitrary order and execution frequency of activities in the sub-process, or it lacks fitness by capturing only single instantiation of sub-processes. For conformance checking, this results in a too high rate of either false positives or false negatives, respectively. In this paper, we propose an extension to well-known process mining techniques, adding the capability of handling multi-instantiated sub-processes to discovery and conformance checking. We evaluate the approach with a real-world data set.