Stage-based discovery of business process models from event logs

An automated process discovery technique generates a process model from an event log recording the execution of a business process. For it to be useful, the generated process model should be as simple as possible, while accurately capturing the behavior recorded in and implied by the event log. Most existing automated process discovery techniques generate flat process models. When confronted to large and complex event logs, these approaches lead to overly complex or inaccurate process models. An alternative is to apply a divide-and-conquer approach by decomposing the process into stages and discovering one model per stage. It turns out however that existing divideand-conquer process discovery approaches often produce less accurate models than flat discovery techniques when applied to real-life event logs. This article investigates the hypothesis that the weaknesses of existing divide-andconquer approaches lies in the way stages are identified. The article contributes a technique to identify stages from an event log based on a modularity measure as well as a technique for discovering process models based on a given stage decomposition. An experimental evaluation shows that the proposed stage identification technique finds stages that are closer to those identified by human experts, while the proposed stage-based process discovery technique outperforms existing flat and divide-and-conquer discovery techniques with respect to well-accepted measures of accuracy and model complexity.