Eindhoven University of Technology
Process mining techniques can be used to discover process models from event data. Often the resulting models are complex due to the variability of the underlying process. Therefore, the authors aim at discovering declarative process models that can deal with such variability. However, for real-life event logs involving dozens of activities and hundreds or thousands of cases, there are often many potential constraints resulting in cluttered diagrams. Therefore, they propose various techniques to prune these models and remove constraints that are not interesting or implied by other constraints.