Abstract
Traditional process mining techniques mainly work with procedural modeling languages (like Petri nets) where all possible orderings of events must be specified explicitly. However, using procedural process models for describing processes working in turbulent environments and characterized by a lot of variability (like healthcare processes) is extremely difficult. Indeed, these processes involve several possible execution paths and representing all of them explicitly makes the process models quickly unreadable. Using declarative process models (like Declare) ensures flexibility in the process description. Even processes that work in environments where participants have more autonomy and are, therefore, more unpredictable can be represented as compact sets of rules. In the process mining tool ProM, there are several plug-ins that can be used for different types of analysis based on Declare ranging from the discovery of Declare models, to conformance checking, to the online monitoring of running process instances.