Process discovery techniques make it possible to automatically derive process models from event data. However, often one is not only interested in discovering the control-flow but also in answering questions like “What do the cases that are late have in common?”, “What characterizes the workers that skip this check activity?”, and “Do people work faster if they have more work?”, etc. Such questions can be answered by combining process mining with classification (e.g., decision tree analysis). Several authors have proposed ad-hoc solutions for specific questions, e.g., there is work on predicting the remaining processing time and recommending activities to minimize particular risks. However, as shown in this paper, it is possible to unify these ideas and provide a general framework for deriving and correlating process characteristics. First, we show how the desired process characteristics can be derived and linked to events. Then, we show that we can derive the selected dependent characteristic from a set of independent characteristics for a selected set of events. This can be done for any process characteristic one can think of. The approach is highly generic and implemented as plug-in for the
framework. Its applicability is demonstrated by using it to answer to a wide range of questions put forward by the UWV (the Dutch Employee Insurance Agency).