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Alarm-Based Prescriptive Process Monitoring

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Business Process Management Forum (BPM 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 329))

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Abstract

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs.

Work supported by the European Community’s FP7 Framework Program under grant n. 603993 (CORE) and by the Estonian Research Council (IUT20-55).

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Notes

  1. 1.

    https://taxxer.github.io/AlarmBasedProcessPrediction/.

  2. 2.

    https://doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b.

  3. 3.

    https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5.

References

  1. Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31

    Chapter  Google Scholar 

  2. Metzger, A., et al.: Comparing and combining predictive business process monitoring techniques. IEEE Trans. Syst. Man Cybern. Syst. 45(2), 276–290 (2015)

    Article  Google Scholar 

  3. Teinemaa, I., Dumas, M., La Rosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. arXiv preprint arXiv:1707.06766 (2017)

  4. Dees, M., de Leoni, M., Mannhardt, F.: Enhancing process models to improve business performance: a methodology and case studies. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 232–251. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_15

    Chapter  Google Scholar 

  5. Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of IJCAI, vol. 17, pp. 973–978. Lawrence Erlbaum Associates Ltd. (2001)

    Google Scholar 

  6. Sheng, V.S., Ling, C.X.: Thresholding for making classifiers cost-sensitive. In: AAAI, pp. 476–481 (2006)

    Google Scholar 

  7. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Proceedings of NIPS, pp. 2546–2554 (2011)

    Google Scholar 

  8. Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems. JMLR 15(1), 3133–3181 (2014)

    Google Scholar 

  9. Olson, R.S., La Cava, W., Mustahsan, Z., Varik, A., Moore, J.H.: Data-driven advice for applying machine learning to bioinformatics problems. In: Proceedings of Biocomputing. World Scientific (2017)

    Google Scholar 

  10. de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)

    Article  Google Scholar 

  11. Zadrozny, B., Elkan, C.: Learning and making decisions when costs and probabilities are both unknown. In: Proceedings of KDD, pp. 204–213. ACM (2001)

    Google Scholar 

  12. Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classif. 10(3), 61–74 (1999)

    Google Scholar 

  13. Turney, P.D.: Types of cost in inductive concept learning. In: Proceedings of the Cost-Sensitive Learning Workshop (2002)

    Google Scholar 

  14. Xing, Z., Pei, J., Philip, S.Y.: Early classification on time series. KAIS 31(1), 105–127 (2012)

    Google Scholar 

  15. Mori, U., Mendiburu, A., Dasgupta, S., Lozano, J.A.: Early classification of time series by simultaneously optimizing the accuracy and earliness. IEEE Trans. Neural Netw. Learn. Syst. (2017). https://doi.org/10.1109/TNNLS.2017.2764939

  16. Dachraoui, A., Bondu, A., Cornuéjols, A.: Early classification of time series as a non myopic sequential decision making problem. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 433–447. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23528-8_27

    Chapter  Google Scholar 

  17. Tavenard, R., Malinowski, S.: Cost-aware early classification of time series. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 632–647. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46128-1_40

    Chapter  Google Scholar 

  18. Metzger, A., Föcker, F.: Predictive business process monitoring considering reliability estimates. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 445–460. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_28

    Chapter  Google Scholar 

  19. Di Francescomarino, C., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. (2017). https://doi.org/10.1109/TSC.2016.2645153

  20. Gröger, C., Schwarz, H., Mitschang, B.: Prescriptive analytics for recommendation-based business process optimization. In: Abramowicz, W., Kokkinaki, A. (eds.) BIS 2014. LNBIP, vol. 176, pp. 25–37. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06695-0_3

    Chapter  Google Scholar 

  21. Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)

    Article  Google Scholar 

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Correspondence to Irene Teinemaa .

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Teinemaa, I., Tax, N., de Leoni, M., Dumas, M., Maggi, F.M. (2018). Alarm-Based Prescriptive Process Monitoring. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management Forum. BPM 2018. Lecture Notes in Business Information Processing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-98651-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-98651-7_6

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