Abstract
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 noise. A good process drift detection method should be accurate, fast, and robust to noise. 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 noise. 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.
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Notes
- 1.
Alpha+ relations define a set of relations between activities which are conflict, concurrency, causality, length-one loop and length-two loop. For their formal definitions, please refer to [20].
- 2.
The G-test of independence is a non-parametric statistical hypothesis test.
- 3.
- 4.
9 drift points are included in each log.
- 5.
\(4148 = 68 \times 6 \times 10 + 68\).
- 6.
We do not add/remove traces into/from the event logs.
- 7.
For example, event 0 refers to the 1st event in the 1st trace, event 1 refers to the 2nd event in the 1st trace ... the last event refers to the last event in the last trace.
- 8.
When an event id is reported, we refer to the id of its corresponding trace.
- 9.
Among all the logs with noise.
- 10.
- 11.
The time includes converting the event log into event stream, forward detection and backward detection. The platform is the same as Sect. 5.4.
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Lu, Y., Chen, Q., Poon, S. (2021). A Robust and Accurate Approach to Detect Process Drifts from Event Streams. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management. BPM 2021. Lecture Notes in Computer Science(), vol 12875. Springer, Cham. https://doi.org/10.1007/978-3-030-85469-0_24
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