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A Tool to Support the Decisions for the Trace Clustering Problem with a Non-compensatory Approach

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Decision Support Systems XIII. Decision Support Systems in An Uncertain World: The Contribution of Digital Twins (ICDSST 2023)

Abstract

Process Discovery and Trace Clustering are used to extract business process-related knowledge from event logs and create models of processes. A non-compensatory approach, involving concordance and discordance settings, can be used to assess trace similarity and form groups. Previous research demonstrated the effectiveness of that approach, but it is time-consuming and requires a deep understanding of the technique’s parameters and desired outcomes. To make the process more efficient, we developed a software tool to assist with parameter definition and analysis of results. The tool provides a user-friendly interface, visual aids, and the ability to adjust parameters to ensure the solution reflects user preferences, allowing users to make more informed decisions. The publicly available tool combines the power and versatility of the R language with the friendly interfaces implemented using the Shiny libraries.

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References

  1. Ahn, B.S., Choi, S.H.: Aggregation of ordinal data using ordered weighted averaging operator weights. Ann. Oper. Res. 201(1), 1–16 (2012). https://doi.org/10.1007/s10479-012-1169-3

    Article  MathSciNet  Google Scholar 

  2. Kassambara, A., Mundt, F.: Factoextra. https://cran.r-project.org/web/packages/factoextra/index.html

  3. Bose, R.P.J.C., van der Aalst, W.M.P.: Context aware trace clustering: towards improving process mining results. In: Proceedings of the SIAM International Conference on Data Mining, SDM 2009, Sparks, Nevada, USA, 30 April–2 May 2009, pp. 401–412. SIAM (2009). https://doi.org/10.1137/1.9781611972795.35

  4. Delias, P., Doumpos, M., Grigoroudis, E., Matsatsinis, N.: A non-compensatory approach for trace clustering. Int. Trans. Oper. Res. 26(5), 1828–1846 (2019). https://doi.org/10.1111/itor.12395. https://onlinelibrary.wiley.com/doi/10.1111/itor.12395

  5. Delias, P., Doumpos, M., Grigoroudis, E., Matsatsinis, N.: Improving the non-compensatory trace-clustering decision process. Int. Trans. Oper. Res. itor.13062 (2021). https://doi.org/10.1111/itor.13062. https://onlinelibrary.wiley.com/doi/10.1111/itor.13062

  6. Poisson-Caillault, E., et al.: sClust. https://cran.r-project.org/web/packages/sClust/index.html

  7. Figueira, J., Roy, B.: Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur. J. Oper. Res. 139(2), 317–326 (2002)

    Article  Google Scholar 

  8. Figueira, J.R., Greco, S., Roy, B., Słowiński, R.: ELECTRE methods: main features and recent developments. In: Zopounidis, C., Pardalos, P.M. (eds.) Handbook of Multicriteria Analysis, vol. 103, pp. 51–89. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-540-92828-7_3

    Chapter  Google Scholar 

  9. Wickham, H.: Reshape2. https://cran.r-project.org/web/packages/reshape2/index.html

  10. Wickham, H.: Tidyverse. https://cloud.r-project.org/web/packages/tidyverse/index.html

  11. Jablonski, S., Röglinger, M., Schönig, S., Wyrtki, K.M.: Multi-perspective clustering of process execution traces. Enterp. Model. Inf. Syst. Architect. (EMISAJ) Int. J. Conceptual Model. 14(2), 1–22 (2019). https://doi.org/10.18417/emisa.14.2

  12. Jacquet-Lagrèze, E., Siskos, Y.: Preference disaggregation: 20 years of MCDA experience. Eur. J. Oper. Res. 130(2), 233–245 (2001)

    Article  Google Scholar 

  13. Kasprzak, P., Mitchell, L., Kravchuk, O., Timmins, A.: Six years of shiny in research - collaborative development of web tools in R. Technical report. arXiv:2101.10948 [cs, stat] (2021)

  14. van der Loo, M., et al.: stringdist. https://cran.r-project.org/web/packages/stringdist/index.html

  15. Dowle, M., et al.: data.table. https://cran.r-project.org/web/packages/data.table/index.html

  16. Cysouw, M.: qlcMatrix. https://cran.r-project.org/web/packages/qlcMatrix/index.html

  17. Mousseau, V., Figueira, J., Naux, J.P.: Using assignment examples to infer weights for ELECTRE TRI method: some experimental results. Eur. J. Oper. Res. 130(2), 263–275 (2001). https://doi.org/10.1016/S0377-2217(00)00041-2. https://www.sciencedirect.com/science/article/pii/S0377221700000412

  18. Cooper, N.: NCmisc. https://cran.r-project.org/web/packages/NCmisc/index.html

  19. Roy, B., Vincke, P.: Pseudo-orders: definition, properties and numerical representation. Math. Soc. Sci. 14(3), 263–274 (1987)

    Article  MathSciNet  Google Scholar 

  20. Roy, B.: Mulcriteria Methodology for Decision Aiding. Kluwer, Dordrecht (1996)

    Book  Google Scholar 

  21. Słowiński, R., Vanderpooten, D.: Similarity relation as a basis for rough approximations. In: Wang, P.P. (ed.) Advances in Machine Intelligence & Soft-Computing, vol. 4, pp. 17–33. Duke University, Department of Electrical Engineering, Durham, NC (1997)

    Google Scholar 

  22. Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace clustering in process mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008. LNBIP, vol. 17, pp. 109–120. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00328-8_11

    Chapter  Google Scholar 

  23. Galili, T., et al.: dendextend. https://cran.r-project.org/web/packages/dendextend/index.html

  24. Pedersen, T.L., et al.: shinyFiles. https://cran.r-project.org/web/packages/shinyFiles/index.html

  25. Zandkarimi, F., Rehse, J.R., Soudmand, P., Hoehle, H.: A generic framework for trace clustering in process mining. In: 2020 2nd International Conference on Process Mining (ICPM). IEEE (2020). https://doi.org/10.1109/icpm49681.2020.00034

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Acknowledgment

This work was supported by the MPhil program “Advanced Technologies in Informatics and Computers”, hosted by the Department of Computer Science, International Hellenic University, Kavala, Greece.

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Correspondence to Nikolaos Zapoglou .

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Zapoglou, N., Delias, P. (2023). A Tool to Support the Decisions for the Trace Clustering Problem with a Non-compensatory Approach. In: Liu, S., Zaraté, P., Kamissoko, D., Linden, I., Papathanasiou, J. (eds) Decision Support Systems XIII. Decision Support Systems in An Uncertain World: The Contribution of Digital Twins . ICDSST 2023. Lecture Notes in Business Information Processing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32534-2_2

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  • DOI: https://doi.org/10.1007/978-3-031-32534-2_2

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