Skip to main content

Object-Centric Predictive Process Monitoring

  • Conference paper
  • First Online:
Service-Oriented Computing – ICSOC 2022 Workshops (ICSOC 2022)

Abstract

Predictive process monitoring approaches aim to make predictions about the future behavior for running instances of business processes, such as next activity or remaining time. Most of these approaches use single object type event logs as if the business process is operating in isolation. Whereas, in an organization, several instances of different processes related to a set of objects can be executed at the same time and may interact with each other. This paper investigate the use of object-centric event logs as they offer information about events and their related objects, allowing access to a global view about the running processes in an organization. We propose an object-centric predictive approach considering interactions between different object types. The proposed approach is evaluated on a publicly available object-centric log. The analysis of the results shows that using additional features (i.e., several object types’ information) can generally help increase prediction performances.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://ocel-standard.org/1.0/running-example.jsonocel.zip.

  2. 2.

    https://github.com/wissam-gherissi/PPM-OC.

  3. 3.

    Softmax function: \(\sigma (z_i) = \frac{e^{z_{i}}}{\sum _{j=1}^K e^{z_{j}}} \ \ \ for\ i=1,2,\dots ,K\).

References

  1. Aalst, W.M.P.: Object-centric process mining: dealing with divergence and convergence in event data. In: Ölveczky, P.C., Salaün, G. (eds.) SEFM 2019. LNCS, vol. 11724, pp. 3–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30446-1_1

    Chapter  Google Scholar 

  2. van der Aalst, W.: Federated process mining: exploiting event data across organizational boundaries, pp. 1–7 (2021). https://doi.org/10.1109/SMDS53860.2021.00011

  3. van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19

    Chapter  Google Scholar 

  4. van der Aalst, W.M., Berti, A.: Discovering object-centric Petri nets. Fund. Inform. 175(1–4), 1–40 (2020)

    MathSciNet  MATH  Google Scholar 

  5. Adams, J.N., Van Der Aalst, W.M.: Precision and fitness in object-centric process mining. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 128–135 (2021). https://doi.org/10.1109/ICPM53251.2021.9576886

  6. Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: predictive business process monitoring with transformer network (2021). https://doi.org/10.48550/ARXIV.2104.00721

  7. 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 

  8. 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 

  9. Galanti, R., de Leoni, M., Navarin, N., Marazzi, A.: Object-centric process predictive analytics (2022). https://doi.org/10.48550/ARXIV.2203.02801

  10. Ghahfarokhi, A.F., Park, G., Berti, A., van der Aalst, W.: OCEL standard (2020). https://ocel-standard.org/1.0/specification.pdf

  11. Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 1–16 (2020). https://doi.org/10.1080/12460125.2020.1780780

  12. Hinkka, M., Lehto, T., Heljanko, K.: Exploiting event log event attributes in RNN based prediction. In: Ceravolo, P., van Keulen, M., Gómez-López, M.T. (eds.) SIMPDA 2018-2019. LNBIP, vol. 379, pp. 67–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46633-6_4

    Chapter  Google Scholar 

  13. Li, G., de Carvalho, R.M., van der Aalst, W.M.P.: Automatic discovery of object-centric behavioral constraint models. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 288, pp. 43–58. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59336-4_4

    Chapter  Google Scholar 

  14. Lin, L., Wen, L., Wang, J.: MM-Pred: a deep predictive model for multi-attribute event sequence, pp. 118–126 (2019). https://doi.org/10.1137/1.9781611975673.14

  15. Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: 2019 International Conference on Process Mining (ICPM), pp. 129–136 (2019). https://doi.org/10.1109/ICPM.2019.00028

  16. Philipp, P., Jacob, R., Robert, S., Beyerer, J.: Predictive analysis of business processes using neural networks with attention mechanism. In: 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 225–230 (2020). https://doi.org/10.1109/ICAIIC48513.2020.9065057

  17. 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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wissam Gherissi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gherissi, W., El Haddad, J., Grigori, D. (2023). Object-Centric Predictive Process Monitoring. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26507-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26506-8

  • Online ISBN: 978-3-031-26507-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics