  Business processes are bound to evolve as a form of adaption to changes, and
such changes are referred as process drifts. Current process drift detection
methods perform well on clean event log data, but the performance can be
tremendously affected by noises. A good process drift detection method should
be accurate, fast, and robust to noises. In this paper, we propose an offline
process drift detection method which identifies each newly observed behaviour
as a candidate drift point and checks if the new behaviour can signify
significant changes to the original process behaviours. In addition, a
bidirectional search method is proposed to accurately locate both the adding
and removing of behaviours. The proposed method can accurately detect drift
points from event logs and is robust to noises. Both artificial and real-life
event logs are used to evaluate our method. Results show that our method can
consistently report accurate process drift time while maintaining a reasonably
fast detection speed.
