Detecting Complex Control-Flow Constructs for Choosing Process Discovery Techniques

Process models are the analytical illustration of an organization’s activity. They are very primordial to map out the current business process of an organization, build a baseline of process enhancement and construct future processes where the enhancements are incorporated. To achieve this, in the field of process mining, algorithms have been proposed to build process models using the information recorded in the event logs. However, for complex process configurations, these algorithms cannot correctly build complex process structures. These structures are invisible tasks, non-free choice constructs, and short loops. The ability of each discovery algorithm in discovering the process constructs is different. In this work, we propose a framework responsible of detecting from event logs the complex constructs existing in the data. By identifying the existing constructs, one can choose the process discovery techniques suitable for the event data in question. The proposed framework has been implemented in ProM as a plugin. The evaluation results demonstrate that the constructs can correctly be identified.