Discovering Multi-perspective Process Models: The Case of Loosely-Structured Processes

Process Mining techniques exploit the information stored in the execution log of a process to extract some high-level process model, useful for analysis or design tasks. Most of these techniques focus on "structural" aspects of the process, in that they only consider what elementary activities were executed and in which ordering. Hence, any other "non-structural" data, usually kept in real log systems (e.g., activity executors, parameter values), are disregarded, yet being a potential source of knowledge. In this paper, we overcome this limitation by proposing a novel approach to the discovery of process models, where the behavior of a process is characterized from both structural and nonstructural viewpoints. Basically, we recognize different executions' classes via a structural clustering approach, and model them with a collection of specific work- flows. Relevant correlations between these classes and non-structural properties are captured by a rule-based classification model, which can be used for both explanation and prediction. In order to empower the versatility of our approach, we also combine it with a pre-processing method, which allows to restructure the log events according to different analysis perspectives, and to study them at the right abstraction level. Interestingly, such an approach reduces the risk of obtaining knotty, "spaghetti-like", process models when analyzing the logs of looselystructured processes consisting of low-level operations that are performed in a more autonomous way than in traditional BPM platforms. Preliminary results on real-life application scenario confirm the validity of the approach