An iterative algorithm for applying the theory of regions in process mining

The research domain of process mining, or more specifically process discovery, aims at constructing a process model as an abstract representation of an event log. The goal is to build a model (i.e. in terms of a Petri net) that (1) can reproduce the log under consideration, and (2) does not allow for much more behaviour than shown in the log. The Theory of Regions can be used to transform a state-based model (such as a transition system) into a Petri net that exactly mimics the behaviour of the transition system. In this paper, we use the Theory of Regions to do process discovery, and we address two problems. First, we show how event logs that do not carry state information can be transformed into transition systems. Second, we deal with the problem of large logs, by showing that the proposed algorithm can be made iterative over the traces in a log, i.e. we change the complexity of the algorithm, such that it requires significantly less space, but more time.