  Detecting undesired process behavior is one of the main tasks of process
mining and various conformance-checking techniques have been developed to this
end. These techniques typically require a normative process model as input,
specifically designed for the processes to be analyzed. Such models are rarely
available, though, and their creation involves considerable manual
effort.However, reference process models serve as best-practice templates for
organizational processes in a plethora of domains, containing valuable
knowledge about general behavioral relations in well-engineered processes.
These general models can thus mitigate the need for dedicated models by
providing a basis to check for undesired behavior. Still, finding a perfectly
matching reference model for a real-life event log is unrealistic because
organizational needs can vary, despite similarities in process execution.
Furthermore, event logs may encompass behavior related to different reference
models, making traditional conformance checking impractical as it requires
aligning process executions to individual models. To still use reference models
for conformance checking, we propose a framework for mining declarative
best-practice constraints from a reference model collection, automatically
selecting constraints that are relevant for a given event log, and checking for
best-practice violations. We demonstrate the capability of our framework to
detect best-practice violations through an evaluation based on real-world
process model collections and event logs.
