  State-of-the-art process discovery methods construct free-choice process
models from event logs. Consequently, the constructed models do not take into
account indirect dependencies between events. Whenever the input behaviour is
not free-choice, these methods fail to provide a precise model. In this paper,
we propose a novel approach for enhancing free-choice process models by adding
non-free-choice constructs discovered a-posteriori via region-based techniques.
This allows us to benefit from the performance of existing process discovery
methods and the accuracy of the employed fundamental synthesis techniques. We
prove that the proposed approach preserves fitness with respect to the event
log while improving the precision when indirect dependencies exist. The
approach has been implemented and tested on both synthetic and real-life
datasets. The results show its effectiveness in repairing models discovered
from event logs.
