Optimizing the trade-off between complexity and conformance in process reduction

While models are recognized to be crucial for business process management, often no model is available at all or available models are not aligned with the actual process implementation. In these contexts, an appealing possibility is recovering the process model from the existing system. Several process recovery techniques have been proposed in the literature. However, the recovered processes are often complex, intricate and thus difficult to understand for business analysts. In this paper, we propose a process reduction technique based on multi-objective optimization, which at the same time minimizes the process complexity and its non-conformances. This allows us to improve the process model understandability, while preserving its completeness with respect to the core business properties of the domain. We conducted a case study based on a real-life e-commerce system. Results indicate that by balancing complexity and conformance our technique produces understandable and meaningful reduced process models. © 2011 Springer-Verlag.