Abstract
Predictive business process monitoring concerns the unfolding of ongoing process instance executions. Recent work in this area frequently applies “blackbox” like methods which, despite delivering high quality prediction results, fail to implement a transparent and understandable prediction generation process, likely, limiting the trust users put into the results. This work tackles this limitation by basing prediction and the related prediction models on well known probability based histogram like approaches. Those enable to quickly grasp, and potentially visualise the prediction results, various alternative futures, and the overall prediction process. Furthermore, the proposed heuristic prediction approach outperforms state-of-the-art approaches with respect to prediction accuracy. This conclusion is drawn based on a publicly available prototypical implementation, real life logs from multiple sources and domains, along with a comparison with multiple alternative approaches.
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Notes
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http://www.xes-standard.org – IEEE 1849-2016 XES Standard.
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DOI: 10.17632/39bp3vv62t.1.
References
Van der Aalst, W.M., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
van Beest, N.R.T.P., Weber, I.: Behavioral classification of business process executions at runtime. In: Dumas, M., Fantinato, M. (eds.) BPM 2016. LNBIP, vol. 281, pp. 339–353. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58457-7_25
Benítez, J.M., Castro, J.L., Requena, I.: Are artificial neural networks black boxes? Trans. Neural Netw. 8(5), 1156–1164 (1997)
Birgé, L., Rozenholc, Y.: How many bins should be put in a regular histogram. ESAIM: Probab. Stat. 10, 24–45 (2006)
Böhmer, K., Rinderle-Ma, S.: Multi instance anomaly detection in business process executions. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 77–93. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_5
Breuker, D., Matzner, M., Delfmann, P., Becker, J.: Comprehensible predictive models for business processes. MIS Q. 40(4), 1009–1034 (2016)
Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D.: Completion time and next activity prediction of processes using sequential pattern mining. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 49–61. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11812-3_5
Cesario, E., Folino, F., Guarascio, M., Pontieri, L.: A cloud-based prediction framework for analyzing business process performances. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 63–80. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45507-5_5
Conforti, R., Fink, S., Manderscheid, J., Röglinger, M.: PRISM – a predictive risk monitoring approach for business processes. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 383–400. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_22
Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)
de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general framework for correlating business process characteristics. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 250–266. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10172-9_16
Di Francescomarino, C., et al.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. (2016). https://ieeexplore.ieee.org/document/7797472
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 252–268. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_15
Durrett, R.: Probability: Theory and Examples. Cambridge University Press, Cambridge (2010)
Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)
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
Ferilli, S., Esposito, F., Redavid, D., Angelastro, S.: Extended process models for activity prediction. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z.W. (eds.) ISMIS 2017. LNCS (LNAI), vol. 10352, pp. 368–377. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60438-1_36
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: Which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27
Gleicher, M.: Explainers: expert explorations with crafted projections. Vis. Comput. Graph. 19(12), 2042–2051 (2013)
Greco, G., Guzzo, A., Pontieri, L.: Mining taxonomies of process models. Data Knowl. Eng. 67(1), 74–102 (2008)
Idri, A., Khoshgoftaar, T.M., Abran, A.: Can neural networks be easily interpreted in software cost estimation? In: Fuzzy Systems, vol. 2, pp. 1162–1167. IEEE (2002)
Klinkmüller, C., van Beest, N.R.T.P., Weber, I.: Towards reliable predictive process monitoring. In: Mendling, J., Mouratidis, H. (eds.) CAiSE 2018. LNBIP, vol. 317, pp. 163–181. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92901-9_15
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. In: Soviet physics doklady, vol. 10, pp. 707–710 (1966)
Mehdiyev, N., et al.: A multi-stage deep learning approach for business process event prediction. In: Business Informatics, vol. 1, pp. 119–128. IEEE (2017)
Pandey, S., Nepal, S., Chen, S.: A test-bed for the evaluation of business process prediction techniques. In: Collaborative Computing, pp. 382–391. IEEE (2011)
Rogge-Solti, A., Weske, M.: Prediction of business process durations using non-Markovian stochastic Petri nets. Inf. Syst. 54, 1–14 (2015)
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
Verenich, I., Nguyen, H., La Rosa, M., Dumas, M.: White-box prediction of process performance indicators via flow analysis. In: Proceedings of the 2017 International Conference on Software and System Process, pp. 85–94. ACM (2017)
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This work has been funded by the Vienna Science and Technology Fund (WWTF) through project ICT15-072.
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Böhmer, K., Rinderle-Ma, S. (2018). Probability Based Heuristic for Predictive Business Process Monitoring. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_5
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