Software cybernetics in BPM: Modeling software behavior as feedback for evolution by a novel discovery method based on augmented event logs

Business Process Management (BPM) is a quickly developing management theory in recent years. The goal of BPM is to improve corporate performance by managing and optimizing the businesses process in and among enterprises. The goal is easier to achieve with the closed-loop feedback mechanism from business process execution to redesign in BPM life cycle, where the business process itself and the set of activities in BPM are viewed as a controlled object and a controller respectively. In this feedback control system, process mining plays an important role in generating feedback of process execution for redesign. However, the existing discovery methods cannot mine certain special structures from execution logs (e.g., implicit dependency, implicit place and short loops) correctly and their mining efficiencies cannot meet the requirements of online process mining. In this paper, we propose a novel discovery method to overcome these challenges based on a kind of augmented event log that will also bring new research directions for process discovery. A case study is presented for introducing how the mined model can be used in business process evolution. Results of experiments are described to show the improvements of the proposed algorithm compared with others.