Abstract
Predictive process analytics enables proactive situational aw-areness by predicting the future of ongoing process instances. To provide a fair comparison between different approaches developed for process prediction, they have been evaluated on publicly available event logs using the next step prediction task. This paper aims to raise awareness of the label ambiguity problem in the context of process prediction by investigating how uncertainty in the ground truth labels affects next step prediction. Label ambiguity arises from cases in the event log that have different continuation options. We argue that the uncertainty created thereby negatively affects evaluation results. To this end, we present a synthetic example that illustrates the problem of label ambiguity in process prediction and quantify the occurrence of ambiguous ground truth labels in common benchmark datasets. Finally, we discuss implications and present ideas that aim to initiate a discussion on how to deal with label ambiguity in process prediction.
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References
van der Aalst, W.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016)
De Koninck, P., vanden Broucke, S., De Weerdt, J.: act2vec, trace2vec, log2vec, and model2vec: representation learning for business processes. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 305–321. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_18
Evermann, J., Rehse, J.-R., Fettke, P.: A deep learning approach for predicting process behaviour at runtime. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 327–338. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58457-7_24
Gao, B.B., Xing, C., Xie, C.W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26(6), 2825–2838 (2017)
Kuss, E., Leopold, H., van der Aa, H., Stuckenschmidt, H., Reijers, H.A.: A probabilistic evaluation procedure for process model matching techniques. Data Knowl. Eng. 117, 393–406 (2018)
Neu, D.A., Lahann, J., Fettke, P.: A systematic literature review on state-of-the-art deep learning methods for process prediction. Artif. Intell. Rev. 55, 801–827 (2021). https://doi.org/10.1007/s10462-021-09960-8
Nolle, T., Seeliger, A., Mühlhäuser, M.: BINet: multivariate business process anomaly detection using deep learning. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 271–287. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_16
Pegoraro, M.: Probabilistic and non-deterministic event data in process mining: embedding uncertainty in process analysis techniques. In: Proceedings of the Doctoral Consortium Papers Presented at the 34th International Conference on Advanced Information Systems Engineering (CAiSE 2022) (2022)
Pfeiffer, P., Lahann, J., Fettke, P.: Multivariate business process representation learning utilizing Gramian angular fields and convolutional neural networks. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 327–344. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85469-0_21
Portolani, P., Brusaferri, A., Ballarino, A., Matteucci, M.: Uncertainty in predictive process monitoring. In: Ciucci, D., et al. (eds.) IPMU 2022. CCIS, vol. 1602, pp. 547–559. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08974-9_44
Rama-Maneiro, E., Vidal, J., Lama, M.: Deep learning for predictive business process monitoring: Review and benchmark. IEEE Trans. Serv. Comput. (2021)
Acknowledgement
Part of this work has been done funded by the project SmartVigilance (FKZ: 01IS20028C) with financial support by the Federal Ministry of Education and Research (BMBF).
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Pfeiffer, P., Lahann, J., Fettke, P. (2023). The Label Ambiguity Problem in Process Prediction. In: Cabanillas, C., Garmann-Johnsen, N.F., Koschmider, A. (eds) Business Process Management Workshops. BPM 2022. Lecture Notes in Business Information Processing, vol 460. Springer, Cham. https://doi.org/10.1007/978-3-031-25383-6_4
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DOI: https://doi.org/10.1007/978-3-031-25383-6_4
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