Context-Aware Predictive Process Monitoring: The Impact of News Sentiment

  • Anton YeshchenkoEmail author
  • Fernando Durier
  • Kate Revoredo
  • Jan Mendling
  • Flavia Santoro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11229)


Predictive business process monitoring is concerned with forecasting how a process is likely to proceed, covering questions such as what is the next activity to expect and what is the remaining time until case completion. Process prediction typically builds on machine learning techniques that leverage past process execution data. A fundamental problem of a process prediction methods is the data acquisition. So far, research on predictive monitoring utilize data, which is internal to the process. In this paper, we present a novel approach of integrating the external context of the business processes into prediction methods. More specifically, we develop a technique that leverages the sentiments of online news for the task of remaining time prediction. Using our prototypical implementation, we carried out experiments that demonstrate the usefulness of this approach and allowing us to draw conclusions about circumstances in which it works best.


Predictive process monitoring External context Sentiment analysis of news 



This work is partially funded by the EU H2020 program under MSCA-RISE agreement 645751 (RISE_BPM), FFG Austrian Research Promotion Agency (project number: 866270), and UNIRIO (PQ-UNIRIO N01/2018).


  1. 1.
    vom Brocke, J., Zelt, S., Schmiedel, T.: On the role of context in business process management. Int. J. Inf. Manag. 36(3), 486–495 (2016)CrossRefGoogle Scholar
  2. 2.
    da Cunha Mattos, T., Santoro, F.M., Revoredo, K., Nunes, V.T.: A formal representation for context-aware business processes. Comput. Ind. 65(8), 1193–1214 (2014)CrossRefGoogle Scholar
  3. 3.
    Di Francescomarino, C., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. (2016)Google Scholar
  4. 4.
    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). Scholar
  5. 5.
    van Dongen, B.: BPI challenge 2012 (2012).
  6. 6.
    van Dongen, B.: BPI challenge 2017 (2017).
  7. 7.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). Scholar
  8. 8.
    Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: Meersman, R. (ed.) OTM 2012. LNCS, vol. 7565, pp. 287–304. Springer, Heidelberg (2012). Scholar
  9. 9.
    Frey, M., Emrich, A., Fettke, P., Loos, P.: Event entry time prediction in financial business processes using machine learning: a use case from loan applications. In: Proceedings of the 51st Hawaii International Conference on System Sciences 2018, pp. 1386–1394. IEEE Computer Society (2018)Google Scholar
  10. 10.
    Jorbina, K., et al.: Nirdizati: a web-based tool for predictive process monitoring (2017)Google Scholar
  11. 11.
    Kireyev, K., Palen, L., Anderson, K.: Applications of topics models to analysis of disaster-related twitter data. In: NIPS Workshop on Applications for Topic Models: Text and Beyond, Whistler, Canada, vol. 1 (2009)Google Scholar
  12. 12.
    de Leoni, M., Mannhardt, F.: Road traffic fine management process (2015).
  13. 13.
    Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)CrossRefGoogle Scholar
  14. 14.
    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). Scholar
  15. 15.
    Marquez-Chamorro, A.E., Resinas, M., Ruiz-Corts, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. PP, 1 (2017)CrossRefGoogle Scholar
  16. 16.
    Marquez-Chamorro, A.E., Resinas, M., Ruiz-Corts, A., Toro, M.: Run-time prediction of business process indicators using evolutionary decision rules. Expert Syst. Appl. 87(C), 1–14 (2017)CrossRefGoogle Scholar
  17. 17.
    Metzger, A., et al.: Comparing and combining predictive business process monitoring techniques. IEEE Trans. Syst. Man Cybern.: Syst. 45(2), 276–290 (2015)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Navarin, N., Vincenzi, B., Polato, M., Sperduti, A.: LSTM networks for data-aware remaining time prediction of business process instances. CoRR (2017)Google Scholar
  19. 19.
    Nielsen, F.Å.: AFINN, March 2011.
  20. 20.
    Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Data-aware remaining time prediction of business process instances. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 816–823, July 2014Google Scholar
  21. 21.
    Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Time and activity sequence prediction of business process instances. CoRR (2016)Google Scholar
  22. 22.
    del Río-Ortega, A., Resinas, M., Cabanillas, C., Ruiz-Cortés, A.: On the definition and design-time analysis of process performance indicators. Inf. Syst. 38(4), 470–490 (2013)CrossRefGoogle Scholar
  23. 23.
    Rosemann, M., Recker, J., Flender, C.: Contextualisation of business processes. Int. J. Bus. Process. Integr. Manag. 3(1), 47–60 (2008)CrossRefGoogle Scholar
  24. 24.
    Senderovich, A., Di Francescomarino, C., Ghidini, C., Jorbina, K., Maggi, F.M.: Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 306–323. Springer, Cham (2017). Scholar
  25. 25.
    Sindhgatta, R., Ghose, A., Dam, H.K.: Context-aware analysis of past process executions to aid resource allocation decisions. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 575–589. Springer, Cham (2016). Scholar
  26. 26.
  27. 27.
    Teinemaa, I., Dumas, M., Maggi, F.M., Di Francescomarino, C.: Predictive business process monitoring with structured and unstructured data. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 401–417. Springer, Cham (2016). Scholar
  28. 28.
    Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. CoRR (2017)Google Scholar
  29. 29.
    Vargiu, E., Urru, M.: Exploiting web scraping in a collaborative filtering-based approach to web advertising. Artif. Intell. Res. 2(1), 44 (2012)CrossRefGoogle Scholar
  30. 30.
    Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., Di Francescomarino, C.: Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 218–229. Springer, Cham (2016). Scholar
  31. 31.
    Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. CoRR (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anton Yeshchenko
    • 1
    Email author
  • Fernando Durier
    • 2
  • Kate Revoredo
    • 2
  • Jan Mendling
    • 1
  • Flavia Santoro
    • 2
  1. 1.Vienna University of Economics and Business (WU)ViennaAustria
  2. 2.Federal University of the State of Rio de Janeiro (UNIRIO)Rio de JaneiroBrazil

Personalised recommendations