From Human Knowledge to Process Models

This contribution suggests a novel approach for a systematic generation of a process model in an informal environment. It is based on the claim that the knowledge about the process to be modelled is distributed in several involved people’s minds. Some people have knowledge about the general process where the single activities are on a high level of abstraction and have to be refined. Other people only know something about some detail of the process, i.e., about the refinement of an activity of the general process which defines a subprocess. Moreover, it is assumed that these domain experts can more easily define instances, i.e. runs, of the general process (of a subprocess, respectively) than the process itself. The approach employs new techniques developed for process mining and Petri net synthesis and adapts these techniques to generate processes from example runs. It is based on a formal definition of partially ordered processes, which allows to proceed in a modular way: Subprocesses and general processes are generated independently. Finally, it is argued that the approach is a suitable first step in a general method for process definitions which is followed by validation techniques.