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European Conference on Advances in Databases and Information Systems

ADBIS 2021: New Trends in Database and Information Systems pp 14–26Cite as

Cost-Sensitive Predictive Business Process Monitoring

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1450))

Abstract

In predictive business process monitoring current and historical process data from event logs is used to predict the evolvement of running process instances. A wide number of machine learning approaches, especially different types of artificial neural networks, are successfully applied for this task. Nevertheless, experimental studies revealed that the resulting predictive models are not able to properly predict non-frequent activities. In this paper we investigate the usefulness of the concept of cost-sensitive learning, which introduces a cost model for different activities to better represent them in the training phase. An evaluation of this concept applied to common predictive monitoring approaches on various real life event logs shows encouraging results.

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Notes

  1. 1.

    The source code can be accessed at https://github.com/mkaep/.

  2. 2.

    https://data.4tu.nl/.

  3. 3.

    i.e., it can be calculated from true positives (TP), true negatives (TN), false positives (FP), and true positive (TP).

References

  1. van der Aalst, W.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)

    Book  Google Scholar 

  2. Al-Jebrni, A.H., Cai, H., Jiang, L.: Predicting the next process event using convolutional neural networks. In: IEEE International Conference on PIC, pp. 332–338 (2018)

    Google Scholar 

  3. Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and Regression Trees. Taylor & Francis (1984)

    Google Scholar 

  4. Camargo, M., Dumas, M., González-Rojas, O.: Learning accurate LSTM models of business processes. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 286–302. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_19

    Chapter  Google Scholar 

  5. Di Mauro, N., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019. LNCS (LNAI), vol. 11946, pp. 348–361. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35166-3_25

    Chapter  Google Scholar 

  6. Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of IJCAI 2001, pp. 973–978. Morgan Kaufmann Publishers Inc., San Francisco (2001)

    Google Scholar 

  7. Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)

    Article  Google Scholar 

  8. Fernández, A., García, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets. Springer, Heidelberg (2018)

    Book  Google Scholar 

  9. He, H., Ma, Y.: Imbalanced Learning: Foundations, Algorithms, and Applications, 1st edn. Wiley-IEEE Press (2013)

    Google Scholar 

  10. Hinkka, M., Lehto, T., Heljanko, K.: Exploiting event log event attributes in RNN based prediction. In: Welzer, T., et al. (eds.) ADBIS 2019. CCIS, vol. 1064, pp. 405–416. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30278-8_40

    Chapter  Google Scholar 

  11. Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6(5), 429–449 (2002)

    Article  Google Scholar 

  12. Käppel, M.: Evaluating predictive business process monitoring approaches on small event logs. arXiv (2021)

    Google Scholar 

  13. Lin, L., Wen, L., Wang, J.: A deep predictive model for multi-attribute event sequence. In: Proceedings of the International Conference on Data Mining 2019, pp. 118–126 (2019)

    Google Scholar 

  14. Mehdiyev, N., Evermann, J., Fettke, P.: A multi-stage deep learning approach for business process event prediction. In: IEEE 19th CBI, pp. 119–128 (2017)

    Google Scholar 

  15. Ortigosa-Hernández, J., Inza, I., Lozano, J.A.: Measuring the class-imbalance extent of multi-class problems. Pattern Recogn. Lett. 98, 32–38 (2017)

    Article  Google Scholar 

  16. Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: Proceedings of ICPM 2019 (2019)

    Google Scholar 

  17. Schönig, S., Jasinski, R., Ackermann, L., Jablonski, S.: Deep learning process prediction with discrete and continuous data features. In: Proceedings of ENASE 2018 (2018)

    Google Scholar 

  18. Sun, Y., Kamel, M.S., Wong, A.K., Wang, Y.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007)

    Article  Google Scholar 

  19. 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

    Chapter  Google Scholar 

  20. Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. TKDD 13(2) (2019)

    Google Scholar 

  21. Theis, J., Darabi, H.: Decay replay mining to predict next process events. IEEE Access 7, 119787–119803 (2019). https://doi.org/10.1109/ACCESS.2019.2937085

    Article  Google Scholar 

  22. Wu, J., Wan, L., Xu, Z.: Algorithms to discover complete frequent episodes in sequences. In: Cao, L., Huang, J.Z., Bailey, J., Koh, Y.S., Luo, J. (eds.) PAKDD 2011. LNCS (LNAI), vol. 7104, pp. 267–278. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28320-8_23

    Chapter  Google Scholar 

  23. Zhang, X., Hu, B.: A new strategy of cost-free learning in the class imbalance problem. IEEE Trans. Knowl. Data Eng. 26(12), 2872–2885 (2014)

    Google Scholar 

  24. Zhou, Z.-H., Liu, X.-Y.: Training cost-sensitive NN with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 63–77 (2006)

    Google Scholar 

  25. Zhou, Z.H., Liu, X.Y.: On multi-class cost-sensitive learning. In: Proceedings of the 21st National Conference on AI, pp. 567–572. AAAI Press (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Computer Science, University of Bayreuth, Bayreuth, Germany

    Martin Käppel & Stefan Jablonski

  2. University of Regensburg, Regensburg, Germany

    Stefan Schönig

Authors
  1. Martin Käppel
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  2. Stefan Jablonski
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  3. Stefan Schönig
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Corresponding author

Correspondence to Martin Käppel .

Editor information

Editors and Affiliations

  1. LIAS/ISAE-ENSMA, Chasseneuil-du-Poitou, France

    Ladjel Bellatreche

  2. University of Tartu, Tartu, Estonia

    Marlon Dumas

  3. Aarhus University, Aarhus, Denmark

    Panagiotis Karras

  4. University of Tartu, Tartu, Estonia

    Raimundas Matulevičius

  5. University of Tartu, Tartu, Estonia

    Ahmed Awad

  6. Humboldt-Universität zu Berlin, Berlin, Germany

    Matthias Weidlich

  7. University of Novi Sad, Novi Sad, Serbia

    Mirjana Ivanović

  8. Linköping University, Linköping, Sweden

    Olaf Hartig

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Käppel, M., Jablonski, S., Schönig, S. (2021). Cost-Sensitive Predictive Business Process Monitoring. In: Bellatreche, L., et al. New Trends in Database and Information Systems. ADBIS 2021. Communications in Computer and Information Science, vol 1450. Springer, Cham. https://doi.org/10.1007/978-3-030-85082-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-85082-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85081-4

  • Online ISBN: 978-3-030-85082-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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