Skip to main content

Time and activity sequence prediction of business process instances

Computing volume 100, pages 1005–1031 (2018)Cite this article

Abstract

The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. We assume this representation according to the Unix epoch time.

  2. We assume that a fixed order is always available for attribute’s values (for example, the lexicographical order).

  3. Source code available at https://www.dropbox.com/s/6ps93qmc81clz74/Polato_etal_Prediction_plugin_prom.zip

  4. The log is a part of the the full log provided by Eindhoven University of Technology.

  5. De Leoni and Mannhardt (2015) Road traffic fine management process. doi:10.4121/ uuid:270fd440-1057-4fb9-89a9-b699b47990f5

  6. Polato (2017) Ticketing. doi:10.4121/uuid:0c60edf1-6f83-4e75-9367-4c63b3e9d5b

References

  1. Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process Lett Rev 10(10):203–224

    Google Scholar 

  2. Bolch G, Greiner S, de Meer H, Trivedi KS (2006) Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. Wiley, New York

    Book  MATH  Google Scholar 

  3. Burattin A (2015) Process mining techniques in business environments. Springer International Publishing, Heidelberg

    Book  Google Scholar 

  4. Ceci M, Lanotte PF, Fumarola F, Cavallo DP, Malerba D (2014) Completion time and next activity prediction of processes using sequential pattern mining. In: DS 2014, pp 49–61

  5. Damerau F (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176

    Article  Google Scholar 

  6. Domhan T, Springenberg JT, Hutter F (2015) Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves, IJCAI’15. AAAI Press, Menlo Park, pp 3460–3468

    Google Scholar 

  7. Drucker H, Burges C, Kaufman L, Smola AJ, Vapnik V (1996) Support vector regression machines. Neural Inf Process Syst 1:155–161

    Google Scholar 

  8. Evermann J, Rehse JR, Fettke P (2017) A deep learning approach for predicting process behaviour at runtime. Springer International Publishing, New York, pp 327–338

    Google Scholar 

  9. Folino F, Guarascio M, Pontieri L (2012) Discovering context-aware models for predicting business process performances. In: OTM 2012, 7565, pp 287–304

  10. Folino F, Guarascio M, Pontieri L (2013) Discovering high-level performance models for ticket resolution Processes. In: OTM 2013, 8185, pp 275–282

  11. Francescomarino CD, Dumas M, Maggi FM, Teinemaa I (2017) Clustering-based predictive process monitoring. In: IEEE transactions on services computing, vol PP

  12. Ghattas J, Soffer P, Peleg M (2014) Improving business process decision making based on past experience. Decis Support Syst 59:93–107

    Article  Google Scholar 

  13. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software. ACM SIGKDD Explor Newslett 11(1):10–18

    Article  Google Scholar 

  14. Hall RW (1990) Queueing methods for services and manufacturing

  15. IEEE Task Force on Process Mining (2012) Process mining manifesto. In: Daniel F, Barkaoui K, Dustdar S (eds) Business process management workshops, vol 99. Lecture notes in business information processing. Springer, Berlin, pp 169–194

  16. Lakshmanan GT, Shamsi D, Doganata YN, Unuvar M, Khalaf RA (2013) Markov prediction model for data-driven semi-structured business processes. Knowledge and information systems

  17. Leitner P, Wetzstein B, Rosenberg F, Michlmayr A, Dustdar S, Leymann F (2009) Runtime prediction of service level agreement violations for composite services. In: International workshops, ICSOC/ServiceWave. Springer, New York, pp 176–186

  18. Leoni MD, Aalst WMPVD, Dees MA (2014) General framework for correlating business process characteristics. In: Business process management, pp 250–266

  19. Leontjeva A, Conforti R, Di Francescomarino C, Dumas M, Maggi FM (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: BPM

  20. Maggi F.M, Di Francescomarino C, Dumas M, Ghidini C (2014) Predictive monitoring of business processes. Springer International Publishing, Cham, pp 457–472

  21. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval, 1st edn. Cambrige University Press, Cambrige

    Book  MATH  Google Scholar 

  22. Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill, Maidenhead

    MATH  Google Scholar 

  23. Tax IN, Verenich MLR, Dumas M (2017) Predictive business process monitoring with lstm neural networks. In: CAiSE

  24. Pandey S, Nepal S, Chen S (2011) A test-bed for the evaluation of business process prediction techniques. In: 7th ICCC: networking, applications and worksharing (CollaborateCom), pp 382–391

  25. Polato M, Sperduti A, Burattin A, de Leoni M (2014) Data-aware remaining time prediction of business process instances. In: IJCNN (WCCI)

  26. Rogge-Solti A, Weske M (2015) Prediction of business process durations using non-markovian stochastic petri nets. Inf Syst 54:1–14

    Article  Google Scholar 

  27. Senderovich A, Weidlich M, Gal A, Mandelbaum A (2015) Queue mining for delay prediction in multi-class service processes. Inf Syst 53:278–295

    Article  Google Scholar 

  28. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  MathSciNet  Google Scholar 

  29. Teinemaa I, Dumas M, Maggi FM, Di Francescomarino C (2016) Predictive business process monitoring with structured and unstructured data. In: La Rosa M, Loos P, Pastor O (eds) BPM 2016. Springer International Publishing, pp 401–417

  30. van der Spoel S, van Keulen M, Amrit C (2013) Process prediction in noisy data sets: a case study in a dutch hospital. Springer, Berlin, pp 60–83

    Google Scholar 

  31. van der Aalst WMP (2011) Process mining–discovery, conformance and enhancement of business processes, 1st edn. Springer, Heidelberg

    MATH  Google Scholar 

  32. van der Aalst WMP, Rubin V, Verbeek E, van Dongen BF, Kindler E, Günther CW (2008) Process mining: a two-step approach to balance between underfitting and overfitting. Softw Syst Model 9(1):87–111

    Article  Google Scholar 

  33. van der Aalst WMP, Schonenberg H, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475

    Article  Google Scholar 

  34. Verbeek E, Buijs JCAM, van Dongen BF, van der Aalst WMP (2010) ProM 6: the process mining toolkit. In: BPM 2010 Demos. Springer, New York, pp. 34–39

  35. Verenich I, Dumas M, Rosa ML, Maggi FM, Francescomarino CD (2016) Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. In: 11th International workshop on BPI 2015. Springer, pp. 218–229

Download references

Acknowledgements

The work reported in this paper is supported by the Eurostars-Eureka project PROMPT (E!6696).

Author information

Authors and Affiliations

  1. University of Padua, Torre Archimede, via Trieste 63, 35121, Padova, Italy

    Mirko Polato & Alessandro Sperduti

  2. DTU, Technical University of Denmark, Kgs. Lyngby, Denmark

    Andrea Burattin

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Massimiliano de Leoni

Authors
  1. Mirko Polato
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandro Sperduti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Burattin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Massimiliano de Leoni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mirko Polato.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Polato, M., Sperduti, A., Burattin, A. et al. Time and activity sequence prediction of business process instances. Computing 100, 1005–1031 (2018). https://doi.org/10.1007/s00607-018-0593-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-018-0593-x

Keywords

  • Process mining
  • Prediction
  • Remaining time
  • Machine learning

Mathematics Subject Classification

  • 68T01