  Process mining aims to diagnose and improve operational processes. Process
mining techniques allow analyzing the event data generated and recorded during
the execution of (business) processes to gain valuable insights. Process
discovery is a key discipline in process mining that comprises the discovery of
process models on the basis of the recorded event data. Most process discovery
algorithms work in a fully automated fashion. Apart from adjusting their
configuration parameters, conventional process discovery algorithms offer
limited to no user interaction, i.e., we either edit the discovered process
model by hand or change the algorithm's input by, for instance, filtering the
event data. However, recent work indicates that the integration of domain
knowledge in (semi-)automated process discovery algorithms often enhances the
quality of the process models discovered. Therefore, this paper introduces
Cortado, a novel process discovery tool that leverages domain knowledge while
incrementally discovering a process model from given event data. Starting from
an initial process model, Cortado enables the user to incrementally add new
process behavior to the process model under construction in a visual and
intuitive manner. As such, Cortado unifies the world of manual process modeling
with that of automated process discovery.
