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Applied Predictive Process Monitoring and Hyper Parameter Optimization in Camunda

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Intelligent Information Systems (CAiSE 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 424))

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Abstract

With the growing amount of data gathered from business processes in recent years, predictive process monitoring (PPM) established as a method to provide valuable insights and make resilient forecasts. However, sophisticated machine learning algorithms and statistical techniques are always equipped with various hyper parameters, which aggravates finding the best configuration for laypeople. Tools like Nirdizati Research (http://research.nirdizati.org/) or apromore (https://apromore.org/) aim to assist in these tasks. Nonetheless these approaches are isolated solutions, which do not integrate into existing productive environments. In this work, a plugin for the widely used workflow and decision automation tool Camunda (https://camunda.com/) is presented which allows creating classifier for the most common operations in PPM. Furthermore, the framework includes a hyper parameter optimization (HPO) and is extensible in prediction types, methods and optimization algorithms.

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Notes

  1. 1.

    http://promtools.org/.

  2. 2.

    https://gitlab.uni-koblenz.de/fg-bks/camunda-ppm-hpo/.

  3. 3.

    https://camunda.com/case-studies/.

  4. 4.

    https://www.promtools.org/doku.php.

  5. 5.

    https://data.4tu.nl/collections/BPI_Challenge_2020/5065541.

  6. 6.

    https://gitlab.uni-koblenz.de/fg-bks/camunda-ppm-hpo/blob/master/Resources/tables.pdf.

References

  1. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  2. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)

    Google Scholar 

  3. Di Francescomarino, C., et al.: Genetic algorithms for hyperparameter optimization in predictive business process monitoring. Inf. Syst. 74, 67–83 (2018)

    Article  Google Scholar 

  4. Di Francescomarino, C., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. 12, 896–909 (2016)

    Article  Google Scholar 

  5. Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27

    Chapter  Google Scholar 

  6. Feurer, M., Hutter, F.: Hyperparameter optimization. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 3–33. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_1

    Chapter  Google Scholar 

  7. Frank, E., Hall, M.A., Witten, I.H.: The WEKA Workbench. M. Kaufmann (2016)

    Google Scholar 

  8. Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)

    Article  Google Scholar 

  9. Hutter, F., Lücke, J., Schmidt-Thieme, L.: Beyond manual tuning of hyperparameters. KI-Künstliche Intelligenz 29(4), 329–337 (2015)

    Article  Google Scholar 

  10. 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). https://doi.org/10.1007/978-3-319-07881-6_31

    Chapter  Google Scholar 

  11. Márquez-Chamorro, A.E., Resinas, M., Ruiz-Cortes, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11(6), 962–977 (2017)

    Article  Google Scholar 

  12. Teinemaa, I., Tax, N., de Leoni, M., Dumas, M., Maggi, F.M.: Alarm-based prescriptive process monitoring. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNBIP, vol. 329, pp. 91–107. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98651-7_6

    Chapter  Google Scholar 

  13. Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 847–855 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute for Information System Research, University of Koblenz-Landau, Koblenz, Germany

    Nico Bartmann, Stefan Hill, Carl Corea, Christoph Drodt & Patrick Delfmann

Authors
  1. Nico Bartmann
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  2. Stefan Hill
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  3. Carl Corea
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  4. Christoph Drodt
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  5. Patrick Delfmann
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Corresponding author

Correspondence to Stefan Hill .

Editor information

Editors and Affiliations

  1. University of Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Axel Korthaus

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Bartmann, N., Hill, S., Corea, C., Drodt, C., Delfmann, P. (2021). Applied Predictive Process Monitoring and Hyper Parameter Optimization in Camunda. In: Nurcan, S., Korthaus, A. (eds) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-79108-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-79108-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79107-0

  • Online ISBN: 978-3-030-79108-7

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