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Predictive Process Monitoring in Apromore

  • Ilya VerenichEmail author
  • Stanislav Mõškovski
  • Simon Raboczi
  • Marlon Dumas
  • Marcello La Rosa
  • Fabrizio Maria Maggi
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 317)

Abstract

This paper discusses the integration of Nirdizati, a tool for predictive process monitoring, into the Web-based process analytics platform Apromore. Through this integration, Apromore’s users can use event logs stored in the Apromore repository to train a range of predictive models, and later use the trained models to predict various performance indicators of running process cases from a live event stream. For example, one can predict the remaining time or the next events until case completion, the case outcome, or the violation of compliance rules or internal policies. The predictions can be presented graphically via a dashboard that offers multiple visualization options, including a range of summary statistics about ongoing and past process cases. They can also be exported into a text file for periodic reporting or to be visualized in third-parties business intelligence tools. Based on these predictions, operations managers may identify potential issues early on, and take remedial actions in a timely fashion, e.g. reallocating resources from one case onto another to avoid that the case runs overtime.

Keywords

Process mining Predictive monitoring Business process Machine learning 

References

  1. 1.
    Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018).  https://doi.org/10.1007/978-3-662-56509-4CrossRefGoogle Scholar
  2. 2.
    Jorbina, K., Rozumnyi, A., Verenich, I., Francescomarino, C.D., Dumas, M., Ghidini, C., Maggi, F.M., Rosa, M.L., Raboczi, S.: Nirdizati: a web-based tool for predictive process monitoring. In: Proceedings of the BPM Demo Track (2017)Google Scholar
  3. 3.
    Leontjeva, A., Conforti, R., Di Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297–313. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-23063-4_21CrossRefGoogle Scholar
  4. 4.
    Márquez-Chamorro, A.E., Resinas, M., Ruiz-Corts, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. PP(99), 1 (2017)CrossRefGoogle Scholar
  5. 5.
    Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. CoRR abs/1707.06766 (2017)Google Scholar
  6. 6.
    Verenich, I.: A general framework for predictive business process monitoring. In: Proceedings of CAiSE 2016 Doctoral Consortium co-located with 28th International Conference on Advanced Information Systems Engineering (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ilya Verenich
    • 1
    • 2
    Email author
  • Stanislav Mõškovski
    • 2
  • Simon Raboczi
    • 3
  • Marlon Dumas
    • 2
  • Marcello La Rosa
    • 3
  • Fabrizio Maria Maggi
    • 2
  1. 1.Queensland University of TechnologyBrisbaneAustralia
  2. 2.University of TartuTartuEstonia
  3. 3.University of MelbourneMelbourneAustralia

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