Abstract
In collaborative business processes that involve multiple organizations, privacy concerns prevent organizations from sharing the raw data of their activities. This makes it challenging to predict remaining time without access to data on completed activities of other organizations. To address this challenge, this research proposes a strategy for predicting remaining time in collaborative business processes, which involve sequential sub-processes executed by different partners, while preserving the privacy of organizations. The proposed strategy involves transferring latent information from precedent sub-processes to the models of latter sub-processes, rather than raw data. Two models were designed to implement this strategy, and the experimental results indicate that the prediction accuracy of the models is comparable to that of models that use raw data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Van der Aalst, W.M., Rubin, V., Verbeek, H., van Dongen, B.F., Kindler, E., Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87–111 (2010)
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., Guizani, M.: A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476–5497 (2021). https://doi.org/10.1109/JIOT.2020.3030072
Abedi, A., Khan, S.S.: FedSL: federated split learning on distributed sequential data in recurrent neural networks. CoRR abs/2011.03180 (2020). https://arxiv.org/abs/2011.03180
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: Processtransformer: predictive business process monitoring with transformer network. arXiv abs/2104.00721 (2021)
Thapa, C., Arachchige, P.C.M., Camtepe, S., Sun, L.: Splitfed: when federated learning meets split learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8485–8493 (2022)
Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M., ter Hofstede, A.H.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)
Elkoumy, G., Pankova, A., Dumas, M.: Mine me but don’t single me out: differentially private event logs for process mining. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 80–87 (2021)
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
Fahrenkrog-Petersen, S.A., van der Aa, H., Weidlich, M.: PRIPEL: privacy-preserving event log publishing including contextual information. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 111–128. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_7
Fan, L., et al.: Rethinking privacy preserving deep learning: how to evaluate and thwart privacy attacks. arXiv abs/2006.11601 (2020)
Hamrouni, I., Lahdhiri, H., Ben Abdellafou, K., Aljuhani, A., Taouali, O.: Anomaly detection for process monitoring based on machine learning technique. Neural Comput. Appl. 35(5), 4073–4097 (2022). https://doi.org/10.1007/s00521-022-07901-2
Márquez-Chamorro, A.E., Resinas, M., Ruiz-Cortés, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11(6), 962–977 (2017)
Navarin, N., Vincenzi, B., Polato, M., Sperduti, A.: LSTM networks for data-aware remaining time prediction of business process instances. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7 (2017). https://doi.org/10.1109/SSCI.2017.8285184
Ogunbiyi, N., Basukoski, A., Chaussalet, T.: Investigating social contextual factors in remaining-time predictive process monitoring-a survival analysis approach. Algorithms 13(11), 267 (2020). https://doi.org/10.3390/a13110267
Park, G., Song, M.: Predicting performances in business processes using deep neural networks. Decis. Support Syst. 129, 113191 (2020). https://doi.org/10.1016/j.dss.2019.113191
Pauwels, S., Calders, T.: Incremental predictive process monitoring: the next activity case. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 123–140. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85469-0_10
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Data-aware remaining time prediction of business process instances. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 816–823 (2014). https://doi.org/10.1109/IJCNN.2014.6889360
Rogge-Solti, A., Weske, M.: Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 389–403. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45005-1_27
Rogge-Solti, A., Weske, M.: Prediction of business process durations using non-markovian stochastic petri nets. Inf. Syst. 54(C), 1–14 (2015). https://doi.org/10.1016/j.is.2015.04.004
Ryu, J., Won, D., Lee, Y.: A study of split learning model. In: 2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM), pp. 1–4 (2022). https://doi.org/10.1109/IMCOM53663.2022.9721798
Senderovich, A., Di Francescomarino, C., Ghidini, C., Jorbina, K., Maggi, F.M.: Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 306–323. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_18
van der Spoel, S., van Keulen, M., Amrit, C.: Process prediction in noisy data sets: a case study in a Dutch hospital. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds.) SIMPDA 2012. LNBIP, vol. 162, pp. 60–83. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40919-6_4
Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477–492. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_30
Taymouri, F., La Rosa, M., Erfani, S.M.: A deep adversarial model for suffix and remaining time prediction of event sequences. In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 522–530 (2021). https://doi.org/10.1137/1.9781611976700.59
Teinemaa, I., Dumas, M., Maggi, F.M., Di Francescomarino, C.: Predictive business process monitoring with structured and unstructured data. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 401–417. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_23
Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 1–57 (2019). https://doi.org/10.1145/3301300
Van Der Aalst, W.: Process mining. Commun. ACM 55(8), 76–83 (2012)
Vandenabeele, J., Vermaut, G., Peeperkorn, J., Weerdt, J.D.: Enhancing stochastic petri net-based remaining time prediction using k-nearest neighbors. arXiv abs/2206.13109 (2022)
Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Trans. Intell. Syst. Technol. (TIST) 10(4), 1–34 (2019)
Wahid, N.A., Bae, H., Adi, T.N., Choi, Y., Iskandar, Y.A.: Parallel-structure deep learning for prediction of remaining time of process instances. Appl. Sci. 11(21), 9848 (2021). https://doi.org/10.3390/app11219848
Wang, J., Chang, V., Yu, D., Liu, C., Ma, X., Yu, D.: Conformance-oriented predictive process monitoring in BPaaS based on combination of neural networks. J. Grid Comput. 20(3), 25 (2022). https://doi.org/10.1007/s10723-022-09613-2
Xu, X., Liu, C., Li, T., Guo, N., Ren, C.G., Zeng, Q.T.: Business process remaining time prediction: an approach based on bidirectional quasi recurrent neural network with attention. Acta Electronica Sinica 50(8), 1975–1984 (2022)
Yin, X., Zhu, Y., Hu, J.: A comprehensive survey of privacy-preserving federated learning: a taxonomy, review, and future directions. ACM Comput. Surv. 54(6), 1–36 (2021). https://doi.org/10.1145/3460427
Acknowledgment
This work is supported by China National Science Foundation (Granted Number 62072301) and the Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality (Granted No. 21511104700).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cao, J., Wang, C., Guan, W., Qian, S., Zhao, H. (2023). Remaining Time Prediction for Collaborative Business Processes with Privacy Preservation. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14420. Springer, Cham. https://doi.org/10.1007/978-3-031-48424-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-48424-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48423-0
Online ISBN: 978-3-031-48424-7
eBook Packages: Computer ScienceComputer Science (R0)