Skip to main content
SpringerLink
Log in

Evaluating Trace Encoding Methods in Process Mining

  • Conference paper
  • First Online:
From Data to Models and Back (DataMod 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12611))

Included in the following conference series:

Abstract

Encoding methods affect the performance of process mining tasks but little work in the literature focused on quantifying their impact. In this paper, we compare 10 different encoding methods from three different families (trace replay and alignment, graph embeddings, and word embeddings) using measures to evaluate the overlaps in the feature space, the accuracy obtained, and the computational resources (time) consumed with a classification task. Across hundreds of event logs representing four variations of five scenarios and five anomalies, it was possible to identify the edge2vec method as the most accurate and effective in reducing class overlapping in the feature space.

This study was financed in part by Coordination for the National Council for Scientific and Technological Development (CNPq) of Brazil - Grant of Project 420562/2018-4 and Fundação Araucária (Paraná, Brazil). It was also partly supported by the program “Piano di sostegno alla ricerca 2019” funded by Università degli Studi di Milano.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/gbrltv/business_process_encoding.

  2. 2.

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

  3. 3.

    https://pm4py.fit.fraunhofer.de/.

  4. 4.

    https://radimrehurek.com/gensim/.

  5. 5.

    https://scikit-learn.org/stable/.

  6. 6.

    https://github.com/eliorc/node2vec.

  7. 7.

    https://github.com/lpfgarcia/ECoL.

  8. 8.

    https://cran.r-project.org/package=ECoL.

References

  1. Bezerra, F., Wainer, J.: Algorithms for anomaly detection of traces in logs of process aware information systems. Inf. Syst. 38(1), 33–44 (2013)

    Article  Google Scholar 

  2. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  3. Bose, R.J.C., Van der Aalst, W.M.: Context aware trace clustering: towards improving process mining results. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 401–412 (2009)

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  5. Burattin, A.: PLG2: multiperspective processes randomization and simulation for online and offline settings (2015)

    Google Scholar 

  6. Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking - Relating Processes and Models. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-99414-7

    Book  Google Scholar 

  7. Ceravolo, P., Tavares, G.M., Junior, S.B., Damiani, E.: Evaluation goals for online process mining: a concept drift perspective. IEEE Trans. Serv. Comput. 1 (2020). https://ieeexplore.ieee.org/abstract/document/9124702

  8. Ceravolo, P., Damiani, E., Torabi, M., Barbon, S.: Toward a new generation of log pre-processing methods for process mining. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNBIP, vol. 297, pp. 55–70. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65015-9_4

    Chapter  Google Scholar 

  9. Chinosi, M., Trombetta, A.: BPMN: an introduction to the standard. Comput. Stand. Interfaces 34(1), 124–134 (2012)

    Article  Google Scholar 

  10. Cummins, L., Bridge, D.: On dataset complexity for case base maintenance. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS (LNAI), vol. 6880, pp. 47–61. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23291-6_6

    Chapter  Google Scholar 

  11. De Koninck, P., vanden Broucke, S., De Weerdt, J.: act2vec, trace2vec, log2vec, and model2vec: representation learning for business processes. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 305–321. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_18

    Chapter  Google Scholar 

  12. Delias, P., Doumpos, M., Grigoroudis, E., Matsatsinis, N.: A non-compensatory approach for trace clustering. Int. Trans. Oper. Res. 26(5), 1828–1846 (2019)

    Article  MathSciNet  Google Scholar 

  13. Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Conformance checking approximation using subset selection and edit distance. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 234–251. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49435-3_15

    Chapter  Google Scholar 

  14. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  15. Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl.-Based Syst. 151, 78–94 (2018)

    Article  Google Scholar 

  16. Hake, P., Zapp, M., Fettke, P., Loos, P.: Supporting business process modeling using RNNs for label classification. In: Frasincar, F., Ittoo, A., Nguyen, L.M., Métais, E. (eds.) NLDB 2017. LNCS, vol. 10260, pp. 283–286. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59569-6_35

    Chapter  Google Scholar 

  17. Ho, T.K., Basu, M.: Complexity measures of supervised classification problems. IEEE Trans. Pattern Anal. Mach. Intell. 24, 289–300 (2002)

    Article  Google Scholar 

  18. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Scalable process discovery with guarantees. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) CAISE 2015. LNBIP, vol. 214, pp. 85–101. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19237-6_6

    Chapter  Google Scholar 

  19. 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_21

    Chapter  Google Scholar 

  20. Lorena, A.C., Garcia, L.P.F., Lehmann, J., Souto, M.C.P., Ho, T.K.: How complex is your classification problem? A survey on measuring classification complexity. ACM Comput. Surv. 52(5), 1–34 (2019)

    Article  Google Scholar 

  21. Mikolov, T., Chen, K., Corrado, G.S., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)

    Google Scholar 

  22. Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: Analyzing business process anomalies using autoencoders. Mach. Learn. 107(11), 1875–1893 (2018). https://doi.org/10.1007/s10994-018-5702-8

    Article  MathSciNet  MATH  Google Scholar 

  23. Nolle, T., Luettgen, S., Seeliger, A., Mühlhäuser, M.: BINet: multi-perspective business process anomaly classification. Inf. Syst. 101458 (2019). https://www.sciencedirect.com/journal/information-systems/special-issue/10419P9FG88

  24. Polato, M., Sperduti, A., Burattin, A., de Leoni, M.D.: Time and activity sequence prediction of business process instances. Computing 100(9), 1005–1031 (2018). https://doi.org/10.1007/s00607-018-0593-x

    Article  MathSciNet  Google Scholar 

  25. Rozinat, A., van der Aalst, W.: Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33(1), 64–95 (2008)

    Article  Google Scholar 

  26. Russell, N., ter Hofstede, A., van der Aalst, W., Mulyar, N.: Workflow control-flow patterns: a revised view. BPM reports (2006)

    Google Scholar 

  27. van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)

    Article  Google Scholar 

  28. Weinberger, K., Dasgupta, A., Langford, J., Smola, A., Attenberg, J.: Feature hashing for large scale multitask learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 1113–1120. Association for Computing Machinery (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Londrina State University (UEL), Londrina, Brazil

    Sylvio Barbon Junior

  2. Università degli Studi di Milano (UNIMI), Milan, Italy

    Paolo Ceravolo & Gabriel Marques Tavares

  3. Khalifa University (KUST), Abu Dhabi, UAE

    Ernesto Damiani

Authors
  1. Sylvio Barbon Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Ceravolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ernesto Damiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gabriel Marques Tavares
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriel Marques Tavares .

Editor information

Editors and Affiliations

  1. University of St Andrews, St Andrews, UK

    Juliana Bowles

  2. ISTI-CNR, Pisa, Italy

    Giovanna Broccia

  3. ISTI-CNR, Pisa, Italy

    Mirco Nanni

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Barbon Junior, S., Ceravolo, P., Damiani, E., Marques Tavares, G. (2021). Evaluating Trace Encoding Methods in Process Mining. In: Bowles, J., Broccia, G., Nanni, M. (eds) From Data to Models and Back. DataMod 2020. Lecture Notes in Computer Science(), vol 12611. Springer, Cham. https://doi.org/10.1007/978-3-030-70650-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-70650-0_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70649-4

  • Online ISBN: 978-3-030-70650-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation