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International Conference on Knowledge Science, Engineering and Management

KSEM 2019: Knowledge Science, Engineering and Management pp 751–762Cite as

A Comparative Analysis Methodology for Process Mining Software Tools

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

Abstract

Process mining is a research discipline situated at the intersection of data mining and computation intelligence on the one hand, and process modelling and analysis on the other hand. The aim of process mining is to use information stored in event logs of information systems in order to discover, monitor, and improve processes [1]. The field of process mining has gained attention over the last years and new process mining software tools, both academic and commercial, have been developed. This paper provides an extensive list of process mining software tools. Moreover, it identifies and describes many criteria that can be used in order to compare process mining software tools. Additionally, this paper introduces a new methodology that can be used for the comparative analysis of any number of process mining software tools, using any number of criteria. Furthermore, this paper describes Analytic Hierarchy Process (AHP), which can be used in order to help users decide which software tool best suits their needs.

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References

  1. 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 

  2. Turner, C.J., Tiwari, A., Olaiya, R., Xu, Y.: Process mining: from theory to practice. Bus. Process Manag. J. 18(3), 493–512 (2012)

    Article  Google Scholar 

  3. Kebede, M.: Comparative evaluation of process mining tools. University of Tartu (2015)

    Google Scholar 

  4. Verstraete, D.: Process mining in practice: comparative study of process mining software, Doctoral dissertation, MS thesis, Faculty of Economics and Business Administration, Ghent University, Ghent, Belgium (2014). https://lib.ugent.be/fulltxt/RUG01/002/165/042/RUG01-002165042_2014_0001_AC.pdf)

  5. Claes, J., Poels, G.: Process mining and the ProM framework: an exploratory survey. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 187–198. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_19

    Chapter  Google Scholar 

  6. Van der Aalst, W.M.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  7. Verbeek, H.M.W., Buijs, J.C.A.M., Van Dongen, B.F., van der Aalst, W.M.: Prom 6: the process mining toolkit. In: Proceedings of BPM Demonstration Track, vol. 615, pp. 34–39 (2010)

    Google Scholar 

  8. Günther, C.W., Rozinat, A.: Disco: discover your processes. BPM (Demos) 940, 40–44 (2012)

    Google Scholar 

  9. Van Der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer Science+Business Media, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3

    Book  MATH  Google Scholar 

  10. Augusto, A.: Automated discovery of process models from event logs: review and benchmark. IEEE Trans. Knowl. Data Eng. 31, 686–705 (2019)

    Article  Google Scholar 

  11. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25

    Chapter  Google Scholar 

  12. Aguirre, S., Parra, C., Alvarado, J.: Combination of process mining and simulation techniques for business process redesign: a methodological approach. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds.) SIMPDA 2012. LNBIP, vol. 162, pp. 24–43. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40919-6_2

    Chapter  Google Scholar 

  13. Aalst, W.M.P.: Business process simulation revisited. In: Barjis, J. (ed.) EOMAS 2010. LNBIP, vol. 63, pp. 1–14. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15723-3_1

    Chapter  Google Scholar 

  14. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Exploring processes and deviations. In: Fournier, F., Mendling, J. (eds.) BPM 2014. LNBIP, vol. 202, pp. 304–316. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15895-2_26

    Chapter  Google Scholar 

  15. Rubin, V.A., Mitsyuk, A.A., Lomazova, I.A., van der Aalst, W.M.: Process mining can be applied to software too! In: Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, p. 57. ACM, September 2014

    Google Scholar 

  16. Van der Aalst, W.M., van Dongen, B.F., Günther, C.W., Rozinat, A., Verbeek, E., Weijters, T.: ProM: the process mining toolkit. BPM (Demos) 489(31), 2 (2009)

    Google Scholar 

  17. van der Aalst, W.M.P., Song, M.: Mining social networks: uncovering interaction patterns in business processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25970-1_16

    Chapter  Google Scholar 

  18. Vázquez-Barreiros, B., Mucientes, M., Lama, M.: Mining Duplicate Tasks from Discovered Processes (2015)

    Google Scholar 

  19. Saaty, T.L.: Analytic hierarchy process. Encycl. Biostatistics 1 (2005)

    Google Scholar 

  20. Majumder, M.: Impact of Urbanization on Water Shortage in Face of Climatic Aberrations. Springer, Singapore (2015). https://doi.org/10.1007/978-981-4560-73-3

    Book  Google Scholar 

  21. Mu, E., Pereyra-Rojas, M.: Understanding the analytic hierarchy process. Practical Decision Making. SOR, pp. 7–22. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-33861-3_2

    Chapter  Google Scholar 

  22. Saaty, T.L.: Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008)

    Google Scholar 

  23. Goepel, K.D: Implementation of an online software tool for the analytic hierarchy process (AHP-OS). Int. J. Anal. Hierarchy Process 10(3) (2018)

    Google Scholar 

  24. https://bpmsg.com/academic/ahp.php. Accessed 20 April 2019

  25. https://bpmsg.com/academic/ahp_altcalc.php?n=3&t=License&c[0]=ProM&c[1]=Disco&c[2]=Celonis+Process+Mining. Accessed 19 April 2019

  26. https://bpmsg.com/academic/ahp-group.php?sc=anEbap. Accessed 20 April 2019

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Acknowledgement

This work has been partly supported by the University of Piraeus Research Center.

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Authors and Affiliations

  1. University of Piraeus, 80, M. Karaoli & A. Dimitriou Street, 18534, Piraeus, Greece

    Panagiotis Drakoulogkonas & Dimitrios Apostolou

Authors
  1. Panagiotis Drakoulogkonas
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  2. Dimitrios Apostolou
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Correspondence to Panagiotis Drakoulogkonas .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Drakoulogkonas, P., Apostolou, D. (2019). A Comparative Analysis Methodology for Process Mining Software Tools. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_66

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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