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Data Mining and Knowledge Discovery

, Volume 32, Issue 5, pp 1306–1338 | Cite as

Temporal stability in predictive process monitoring

  • Irene Teinemaa
  • Marlon Dumas
  • Anna Leontjeva
  • Fabrizio Maria Maggi
Article
  • 283 Downloads
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2018

Abstract

Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments where users make decisions and take actions in response to the predictions they receive, it is equally important to optimize the stability of the successive predictions made for each case. To this end, this paper defines a notion of temporal stability for binary classification tasks in predictive process monitoring and evaluates existing methods with respect to both temporal stability and accuracy. We find that methods based on XGBoost and LSTM neural networks exhibit the highest temporal stability. We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability. Finally, we show that time series smoothing techniques can further enhance temporal stability at the expense of slightly lower accuracy.

Keywords

Predictive process monitoring Early sequence classification Stability 

Notes

Acknowledgements

This research was partly funded by the Estonian Research Council (Grant IUT20-55).

References

  1. Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281–305MathSciNetzbMATHGoogle Scholar
  2. Bousquet O, Elisseeff A (2002) Stability and generalization. J Mach Learn Res 2:499–526MathSciNetzbMATHGoogle Scholar
  3. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140zbMATHGoogle Scholar
  4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefzbMATHGoogle Scholar
  5. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 785–794Google Scholar
  6. de Leoni M, van der Aalst WM, Dees M (2016) A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf Syst 56:235–257CrossRefGoogle Scholar
  7. Di Francescomarino C, Dumas M, Maggi FM, Teinemaa I (2017) Clustering-based predictive process monitoring. IEEE Trans Serv Comput.  https://doi.org/10.1109/TSC.2016.2645153
  8. Dumas M, La Rosa M, Mendling J, Reijers HA (2013) Fundamentals of business process management. Springer, BerlinCrossRefGoogle Scholar
  9. Elisseeff A, Evgeniou T, Pontil M (2005) Stability of randomized learning algorithms. J Mach Learn Res 6:55–79MathSciNetzbMATHGoogle Scholar
  10. Evermann J, Rehse JR, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst 100:129–40CrossRefGoogle Scholar
  11. Fernández-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems. J Mach Learn Res 15(1):3133–3181MathSciNetzbMATHGoogle Scholar
  12. Guo C, Pleiss G, Sun Y, Weinberger KQ (2017) On calibration of modern neural networks. arXiv preprint arXiv:1706.04599
  13. Lakshmanan GT, Duan S, Keyser PT, Curbera F, Khalaf R (2010) Predictive analytics for semi-structured case oriented business processes. In: International conference on business process management. Springer, Berlin, pp 640–651Google Scholar
  14. Leontjeva A, Conforti R, Di Francescomarino C, Dumas M, Maggi FM (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: International conference on business process management. Springer, Berlin, pp 297–313Google Scholar
  15. Lin YF, Chen HH, Tseng VS, Pei J, et al (2015) Reliable early classification on multivariate time series with numerical and categorical attributes. In: PAKDD (1), pp 199–211Google Scholar
  16. Liu CB, Chamberlain BP, Little DA, Cardoso  (2017) Generalising random forest parameter optimisation to include stability and cost. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, pp 102–113Google Scholar
  17. Maggi FM, Di Francescomarino C, Dumas M, Ghidini C (2014) Predictive monitoring of business processes. In: International conference on advanced information systems engineering. Springer, Berlin, pp 457–472Google Scholar
  18. Marquez-Chamorro AE, Resinas M, Ruiz-Cortes A (2017) Predictive monitoring of business processes: a survey. IEEE Trans Serv ComputGoogle Scholar
  19. Metzger A, Leitner P, Ivanovic D, Schmieders E, Franklin R, Carro M, Dustdar S, Pohl K (2015) Comparing and combining predictive business process monitoring techniques. IEEE Trans Syst Man Cybern Syst 45(2):276–290CrossRefGoogle Scholar
  20. Mori U, Mendiburu A, Keogh E, Lozano JA (2017) Reliable early classification of time series based on discriminating the classes over time. Data Min Knowl Discov 31(1):233–263MathSciNetCrossRefGoogle Scholar
  21. Niculescu-Mizil A, Caruana R (2005) Predicting good probabilities with supervised learning. In: Proceedings of the 22nd international conference on machine learning, ACM, pp 625–632Google Scholar
  22. Olson RS, La Cava W, Mustahsan Z, Varik A, Moore JH (2018) Data-driven advice for applying machine learning to bioinformatics problems. Pac Symp Biocomput 23:192–203Google Scholar
  23. Osborne J (2013) Dealing with missing or incomplete data: debunking the myth of emptiness. In: Best practices in data cleaning: a complete guide to everything you need to do before and after collecting your data. Sage, Thousand Oaks, pp 105–138CrossRefGoogle Scholar
  24. Parrish N, Anderson HS, Gupta MR, Hsiao DY (2013) Classifying with confidence from incomplete information. J Mach Learn Res 14(1):3561–3589MathSciNetzbMATHGoogle Scholar
  25. Platt J et al (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10(3):61–74Google Scholar
  26. Polato M, Sperduti A, Burattin A, de Leoni M (2014) Data-aware remaining time prediction of business process instances. In: International joint conference on IEEE neural networks (IJCNN), pp 816–823Google Scholar
  27. Rogge-Solti A, Weske M (2013) Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: International conference on service-oriented computing (ICSOC). Springer, Berlin, pp 389–403Google Scholar
  28. Santos T, Kern R (2016) A literature survey of early time series classification and deep learning. In: Proceedings of the 1st international workshop on science, application and methods in industry 4.0 co-located with i-KNOW 2016. CEUR workshop proceedings, vol 1793. CEUR-WS.orgGoogle Scholar
  29. Schafer JL, Graham JW (2002) Missing data: our view of the state of the art. Psychol methods 7(2):147CrossRefGoogle Scholar
  30. Senderovich A, Di Francescomarino C, Ghidini C, Jorbina K, Maggi FM (2017) Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: International conference on business process management. Springer, Berlin, pp 306–323Google Scholar
  31. Tax N, Verenich I, La Rosa M, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: International conference on advanced information systems engineering. Springer, Berlin, pp 477–492Google Scholar
  32. Tax N, Verenich I, La Rosa M, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: International conference on advanced information systems engineering. Springer, Berlin, pp 477–492Google Scholar
  33. Teinemaa I, Dumas M, La Rosa M, Maggi FM (2017) Outcome-oriented predictive process monitoring: review and benchmark. arXiv preprint arXiv:1707.06766
  34. van der Aalst WM (2016) Process mining: data science in action. Springer, BerlinCrossRefGoogle Scholar
  35. van Dongen BF, Crooy RA, van der Aalst WM (2008) Cycle time prediction: when will this case finally be finished? In: OTM confederated international conferences“ on the move to meaningful internet systems”. Springer, pp 319–336Google Scholar
  36. Xing Z, Pei J, Dong G, Yu PS (2008) Mining sequence classifiers for early prediction. In: Proceedings of the 2008 SIAM international conference on data mining, SIAM, pp 644–655Google Scholar
  37. Xing Z, Pei J, Philip SY (2012) Early classification on time series. Knowl Inf Syst 31(1):105–127CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  1. 1.University of TartuTartuEstonia

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