Process Discovery from Low-Level Event Logs

The discovery of a control-flow model for a process is here faced in a challenging scenario where each trace in the given log LE encodes a sequence of low-level events without referring to the process' activities. To this end, we define a framework for inducing a process model that describes the process' behavior in terms of both activities and events, in order to effectively support the analysts (who typically would find more convenient to reason at the abstraction level of the activities than at that of low-level events). The proposed framework is based on modeling the generation of LE with a suitable Hidden Markov Model (HMM), from which statistics on precedence relationships between the hidden activities that triggered the events reported in LE are retrieved. These statistics are passed to the well-known Heuristics Miner algorithm, in order to produce a model of the process at the abstraction level of activities. The process model is eventually augmented with probabilistic information on the mapping between activities and events, encoded in the discovered HMM. The framework is formalized and experimentally validated in the case that activities are "atomic" (i.e., an activity instance triggers a unique event), and several variants and extensions (including the case of "composite" activities) are discussed.