ProDiGy : Human-in-the-loop process discovery

Abstract

Process mining is a discipline that combines the two worlds of business process management and data mining. The central component of process mining is a graphical process model that provides an intuitive way of capturing the logical flow of a process. Traditionally, these process models are either modeled by a user relying on domain expertise only; or discovered automatically by relying entirely on event data. In an attempt to address this apparent gap between user-driven and data-driven process discovery, we present ProDiGy, an alternative approach that enables interactive process discovery by allowing the user to actively steer process discovery. ProDiGy provides the user with automatic recommendations to edit a process model, and quantify and visualize the impact of each recommendation. We evaluated ProDiGy (i) objectively by comparing it with automated discovery approaches and (ii) subjectively by performing a user study with healthcare researchers. Our results show that ProDiGy enables inclusion of domain knowledge in process discovery, which leads to an improvement of the results over the traditional process discovery techniques. Furthermore, we found that ProDiGy also increases the comprehensibility of a process model by providing the user with more control over the discovery of the process model.