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Analyzing How Process Mining Reports Answer Time Performance Questions

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Business Process Management (BPM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13420))

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Abstract

The advances in process mining have provided process analysts with a plethora of different algorithms and techniques that can be used for different purposes. Previous research has studied the relationship between these techniques and business questions, but how process analysts use them to answer specific questions is not fully understood yet. We are interested in discovering how process analysts respond to specific business questions related to time performance. We have coded 110 answers to time performance questions in more than 60 process mining reports. As a result, we have identified 55 different operations with 137 variants used in them. We have analyzed the types of answers and their similarities, and examined how contextual information as well as existing process mining support may affect them. The results of the study provide an overview of the current state-of-practice to answer time performance questions and unveil opportunities to improve process mining tools and the way these questions are answered.

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References

  1. van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Berlin (2016). https://doi.org/10.1007/978-3-662-49851-4

  2. van der Aalst, W.M.P.: A practitioner’s guide to process mining: Limitations of the directly-follows graph. Procedia Comput. Sci. 164, 321–328 (2019)

    Article  Google Scholar 

  3. Bozkaya, M., Gabriels, J., van der Werf, J.M.: Process diagnostics: a method based on process mining. In: eKNOW, pp. 22–27 (2009)

    Google Scholar 

  4. Cabanillas, C., Ackermann, L., Schönig, S., Sturm, C., Mendling, J.: The RALph miner for automated discovery and verification of resource-aware process models. Softw. Syst. Model. 19(6), 1415–1441 (2020). https://doi.org/10.1007/s10270-020-00820-7

    Article  Google Scholar 

  5. Capitán-Agudo, C., Salas-Urbano, M., Cabanillas, C., Resinas, M.: BPI challenge analysis: how are time performance questions answered, March 2022. https://github.com/isa-group/bpi-challenge-performance-analysis

  6. van Dongen, B.: BPI Challenge 2015. 4TU.ResearchData, May 2015. https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1

  7. van Dongen, B.: BPI Challenge 2017. 4TU.ResearchData, February 2017. https://doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310b

  8. van Dongen, B.: BPI Challenge 2019. 4TU.ResearchData, January 2019. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1

  9. van Dongen, B.: BPI Challenge 2020. 4TU.ResearchData, March 2020. https://doi.org/10.4121/uuid:52fb97d4-4588-43c9-9d04-3604d4613b51

  10. van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.: PM\(^2\): a process mining project methodology. In: CAiSE, pp. 297–313 (2015)

    Google Scholar 

  11. Emamjome, F., Andrews, R., ter Hofstede, A.H.: A Case Study Lens on Process Mining in Practice. In: OTM Conferences. pp. 127–145 (2019)

    Google Scholar 

  12. Graafmans, T., Turetken, O., Poppelaars, H., Fahland, D.: Process mining for six sigma. Bus. Inf. Syst. Eng. 63(3), 277–300 (2021)

    Article  Google Scholar 

  13. Hompes, B.F.A., Maaradji, A., Rosa, M.L., Dumas, M., Buijs, J.C.A.M., Aalst, W.M.P.v.d.: Discovering causal factors explaining business process performance variation. In: CAiSE, pp. 177–192 (2017)

    Google Scholar 

  14. Klinkmüller, C., Müller, R., Weber, I.: Mining process mining practices: an exploratory characterization of information needs in process analytics. In: BPM, pp. 322–337 (2019)

    Google Scholar 

  15. de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)

    Article  Google Scholar 

  16. Lopes, I.F., Ferreira, D.R.: A survey of process mining competitions: the BPI challenges 2011–2018. In: BPM Workshops, pp. 263–274 (2019)

    Google Scholar 

  17. Low, W.Z., van der Aalst, W.M.P., ter Hofstede, A.H.M., Wynn, M.T., De Weerdt, J.: Change visualisation: analysing the resource and timing differences between two event logs. Inf. Syst. 65(Supplement C), 106–123 (2017)

    Google Scholar 

  18. Maggi, F.M.: Discovering metric temporal business constraints from event logs. In: Johansson, B., Andersson, B., Holmberg, N. (eds.) BIR 2014. LNBIP, vol. 194, pp. 261–275. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11370-8_19

    Chapter  Google Scholar 

  19. Revoredo, K., Djurica, D., Mendling, J.: A study into the practice of reporting software engineering experiments. Emp. Softw. Eng. 26(6), 1–50 (2021). https://doi.org/10.1007/s10664-021-10007-3

    Article  Google Scholar 

  20. Richter, F., Seidl, T.: TESSERACT: time-drifts in event streams using series of evolving rolling averages of completion times. In: BPM, pp. 289–305 (2017)

    Google Scholar 

  21. Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: A literature review. J. Biomed. Inform. 61, 224–236 (2016)

    Article  Google Scholar 

  22. Senderovich, A., et al.: Conformance checking and performance improvement in scheduled processes: a queueing-network perspective. Inf. Syst. 62, 185–206 (2016)

    Article  Google Scholar 

  23. Stol, K., Ralph, P., Fitzgerald, B.: Grounded theory in software engineering research: a critical review and guidelines. In: ICSE, pp. 120–131 (2016)

    Google Scholar 

  24. Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Am. J. Plant Sci. 5, 1–34 (1948)

    Google Scholar 

  25. Wynn, M.T., et al.: ProcessProfiler3D: a visualisation framework for log-based process performance comparison. Decis. Support Syst. 100(Supplement C), 93–108 (2017)

    Google Scholar 

  26. Zandkarimi, F., Decker, P., Rehse, J.R.: Fig4PM: a library for calculating event log measures. In: ICPM Doctoral Consortium and Demo Track, pp. 27–28 (2021)

    Google Scholar 

  27. Zerbato, F., Soffer, P., Weber, B.: Initial insights into exploratory process mining practices. In: BPM Forum, pp. 145–161 (2021)

    Google Scholar 

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Acknowledgements

This work has been funded by grants RTI2018-100763-J-I00 and RTI2018-101204-B-C22 funded by MCIN/AEI/10.13039/501100011033/ and ERDF A way of making Europe; grant P18-FR-2895 funded by Junta de Andalucía/FEDER, UE; and grant US-1381595 (US/JUNTA/FEDER,UE).

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Authors and Affiliations

  1. SCORE Lab, Universidad de Sevilla, Seville, Spain

    Carlos Capitán-Agudo, María Salas-Urbano, Cristina Cabanillas & Manuel Resinas

  2. I3US Institute, Universidad de Sevilla, Seville, Spain

    Cristina Cabanillas & Manuel Resinas

Authors
  1. Carlos Capitán-Agudo
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  2. María Salas-Urbano
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  3. Cristina Cabanillas
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  4. Manuel Resinas
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Corresponding author

Correspondence to Carlos Capitán-Agudo .

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Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Claudio Di Ciccio

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco Dijkman

  3. Universidad de Sevilla, Seville, Spain

    Adela del Río Ortega

  4. Technical University of Munich, Garching, Germany

    Stefanie Rinderle-Ma

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Capitán-Agudo, C., Salas-Urbano, M., Cabanillas, C., Resinas, M. (2022). Analyzing How Process Mining Reports Answer Time Performance Questions. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. Lecture Notes in Computer Science, vol 13420. Springer, Cham. https://doi.org/10.1007/978-3-031-16103-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-16103-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16102-5

  • Online ISBN: 978-3-031-16103-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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