Skip to main content
SpringerLink
Log in

A Generic Framework for Attribute-Driven Hierarchical Trace Clustering

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 397))

Included in the following conference series:

Abstract

The execution of business processes often entails a specific process execution context, e.g. a customer, service or product. Often, the corresponding event data logs indicators of such an execution context, e.g., a customer type (bronze, silver, gold or platinum). Typically, variations in the execution of a process exist for the different execution context of a process. To gain a better understanding of the global process execution, it is interesting to study the behavioral (dis)similarity between different execution contexts of a process. However, in real business settings, the exact number of execution contexts might be too large to analyze manually. At the same time, current trace clustering techniques do not take process type information into account, i.e., they are solely behaviorally driven. Hence, in this paper, we present a hierarchical data-attribute-driven trace clustering framework that allows us to compare the behavior of different groups of traces. Our evaluation shows that the incorporation of data-attributes in trace clustering yields interesting novel process insights.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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/caoyukun0430/pm4py-source/tree/yukun_paper and https://github.com/caoyukun0430/pm4py-ws/tree/dev-yukun.

  2. 2.

    We formalize the abstractions on sets of sequences over \(\mathcal {A}\), rather than elements \(c{\in }\mathcal {C}\). Note that, given \(c{\in }\mathcal {C}\), we are able to access the trace-view by means of \(\pi _{\texttt {trace}}(c)\).

References

  1. Aalst, W.: Data science in action. Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1

    Chapter  Google Scholar 

  2. Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. IEEE Trans. Knowl. Data Eng. 31(4), 686–705 (2019)

    Article  Google Scholar 

  3. Bae, J., Caverlee, J., Liu, L., Yan, H.: Process mining by measuring process block similarity. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 141–152. Springer, Heidelberg (2006). https://doi.org/10.1007/11837862_15

    Chapter  Google Scholar 

  4. Berti, A., van Zelst, S.J., van der Aalst, W.M.P.: Process mining for python (PM4Py): bridging the gap between process-and data science. In: Proceedings of the ICPM Demo Track 2019, co-located with 1st International Conference on Process Mining (ICPM 2019), Aachen, Germany, June 24–26, 2019, pp. 13–16 (2019)

    Google Scholar 

  5. 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. SIAM (2009)

    Google Scholar 

  6. Bose, R.P.J.C., van der Aalst, W.M.P.: Trace clustering based on conserved patterns: towards achieving better process models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 170–181. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_16

    Chapter  Google Scholar 

  7. Buijs, J.: Receipt phase of an environmental permit application process (‘wabo’), coselog project (2014). https://doi.org/10.4121/UUID:A07386A5-7BE3-4367-9535-70BC9E77DBE6

  8. Buijs, J.: Environmental permit application process (‘WABO’), CoSeLoG project. Eindhoven Univ. Technol. Dataset (2014). https://doi.org/10.4121/uuid:26aba40d-8b2d-435b-b5af-6d4bfbd7a270

    Article  Google Scholar 

  9. Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-based clustering and visualization of navigation patterns on a web site. Data Mining Knowl. Disc. 7(4), 399–424 (2003)

    Article  MathSciNet  Google Scholar 

  10. Conforti, R., La Rosa, M., ter Hofstede, A.H.M.: Filtering out infrequent behavior from business process event logs. IEEE Trans. Knowl. Data Eng. 29(2), 300–314 (2017)

    Article  Google Scholar 

  11. De Koninck, P., Nelissen, K., Baesens, B., vanden Broucke, S., Snoeck, M., De Weerdt, J.: An approach for incorporating expert knowledge in trace clustering. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 561–576. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_35

    Chapter  Google Scholar 

  12. de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M., van Dongen, B.F., Saccà, D.: Process mining based on clustering: a quest for precision. In: ter Hofstede, A., Benatallah, B., Paik, H.-Y. (eds.) BPM 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78238-4_4

    Chapter  Google Scholar 

  13. van Dongen, B.F.: Bpi challenge 2012 (2012). https://doi.org/10.4121/UUID:3926DB30-F712-4394-AEBC-75976070E91F

  14. van Dongen, B.F.: Bpi challenge 2017 (2017). https://doi.org/10.4121/UUID:5F3067DF-F10B-45DA-B98B-86AE4C7A310B

  15. van Dongen, B.F., Borchert, F.: Bpi challenge 2018 (2018). https://doi.org/10.4121/uuid:3301445f-95e8-4ff0-98a4-901f1f204972

  16. Sani, M.F., van Zelst, S.J., van der Aalst, W.M.P.: Improving process discovery results by filtering outliers using conditional behavioural probabilities. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 216–229. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_16

    Chapter  Google Scholar 

  17. Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Applying sequence mining for outlier detection in process mining. In: CoopIS, C&TC, and ODBASE 2018, Valletta, Malta, October 22–26, 2018, Proceedings, Part II, pp. 98–116 (2018)

    Google Scholar 

  18. Fani Sani, M., van Zelst, S.J., van der Aalst, W.M.P.: Repairing outlier behaviour in event logs. In: Abramowicz, W., Paschke, A. (eds.) BIS 2018. LNBIP, vol. 320, pp. 115–131. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93931-5_9

    Chapter  Google Scholar 

  19. Ferreira, D., Zacarias, M., Malheiros, M., Ferreira, P.: Approaching process mining with sequence clustering: experiments and findings. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 360–374. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75183-0_26

    Chapter  Google Scholar 

  20. Greco, G., Guzzo, A., Pontieri, L., Sacca, D.: Discovering expressive process models by clustering log traces. IEEE Trans. Knowl. Data Eng. 18(8), 1010–1027 (2006)

    Article  Google Scholar 

  21. Hompes, B., Buijs, J., Van der Aalst, W., Dixit, P., Buurman, J.: Discovering deviating cases and process variants using trace clustering. In: 27th Benelux Conference on Artificial Intelligence (BNAIC), November, pp. 5–6 (2015)

    Google Scholar 

  22. Jung, J.-Y., Bae, J.: Workflow clustering method based on process similarity. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3981, pp. 379–389. Springer, Heidelberg (2006). https://doi.org/10.1007/11751588_40

    Chapter  Google Scholar 

  23. Jung, J.Y., Bae, J., Liu, L.: Hierarchical clustering of business process models. Int. J. Innovative Comput. Inf. Control 5(12), 1349–4198 (2009)

    Google Scholar 

  24. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Phys. Doklady. 10, 707–710 (1966)

    MathSciNet  Google Scholar 

  25. Lu, X., Tabatabaei, S.A., Hoogendoorn, M., Reijers, H.A.: Trace clustering on very large event data in healthcare using frequent sequence patterns. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 198–215. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_14

    Chapter  Google Scholar 

  26. Luengo, D., Sepúlveda, M.: Applying clustering in process mining to find different versions of a business process that changes over time. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 153–158. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_15

    Chapter  Google Scholar 

  27. Murtagh, F., Contreras, P.: Algorithms for hierarchical clustering: an overview, II. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 7(6) (2017)

    Google Scholar 

  28. 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 

  29. Song, M., Yang, H., Siadat, S.H., Pechenizkiy, M.: A comparative study of dimensionality reduction techniques to enhance trace clustering performances. Expert Syst. Appl. 40(9), 3722–3737 (2013)

    Article  Google Scholar 

  30. Sun, Y., Bauer, B.: A novel top-down approach for clustering traces. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 331–345. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_21

    Chapter  Google Scholar 

  31. De Weerdt, J., vanden Broucke, S.K.L.M., Vanthienen, J., Baesens, B., : Active trace clustering for improved process discovery. IEEE Trans. Knowl. Data Eng. 25(12), 2708–2720 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fraunhofer Institute for Applied Information Technology (FIT), Sankt Augustin, Germany

    Sebastiaan J. van Zelst

  2. Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

    Sebastiaan J. van Zelst & Yukun Cao

Authors
  1. Sebastiaan J. van Zelst
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yukun Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastiaan J. van Zelst .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Seville, Spain

    Adela Del Río Ortega

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  3. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Flávia Maria Santoro

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

van Zelst, S.J., Cao, Y. (2020). A Generic Framework for Attribute-Driven Hierarchical Trace Clustering. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66498-5_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66497-8

  • Online ISBN: 978-3-030-66498-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation