Skip to main content

Attribute-Driven Case Notion Discovery for Unlabeled Event Logs

  • 558 Accesses

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 436)

Abstract

Event logs can be analyzed using various process mining techniques (e.g., process discovery) to obtain valuable information about the actual behavior of business process executions. Typically, these techniques rely on the presence of a case identifier linking events to process instances. However, if the process involves information systems that do not record events in a process-oriented manner, a clear case identifier may be missing, resulting in an unlabeled event log. While some approaches already address the challenge of inferring case identifiers for unlabeled event logs, most of them provide limited support for cyclic behavior without additional inputs. This paper proposes a three-step approach to correlate events with case identifiers for unlabeled event logs originating from processes with cyclic behavior. While evaluating the accuracy of our approach with two real-world event logs (MIMIC-IV and Road Traffic Fine Management), we show that our approach, compared to the existing ones, detects cyclic behavior and correlates events closer to the original process instances without additional inputs.

Keywords

  • Process mining
  • Unlabeled event log
  • Case notion discovery

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-94343-1_9
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-94343-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Notes

  1. 1.

    https://github.com/diogoff/unlabelled-event-logs.

  2. 2.

    https://github.com/tom-lichtenstein/attribute-driven-case-notion-discovery.

References

  1. IEEE standard for extensible event stream (XES) for achieving interoperability in event logs and event streams. IEEE Std 1849–2016, pp. 1–50 (2016). https://doi.org/10.1109/IEEESTD.2016.7740858

  2. van der Aalst, W.M.P.: Process-aware information systems: lessons to be learned from process mining. Trans. Petri Nets Model. Concurr. 2, 1–26 (2009). https://doi.org/10.1007/978-3-642-00899-3_1

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

  4. Bano, D., Weske, M.: Discovering data models from event logs. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds.) ER 2020. LNCS, vol. 12400, pp. 62–76. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62522-1_5

    CrossRef  Google Scholar 

  5. Bayomie, D., Awad, A., Ezat, E.: Correlating unlabeled events from cyclic business processes execution. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 274–289. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39696-5_17

    CrossRef  Google Scholar 

  6. Bayomie, D., Di Ciccio, C., La Rosa, M., Mendling, J.: A probabilistic approach to event-case correlation for process mining. In: Laender, A.H.F., Pernici, B., Lim, E.-P., de Oliveira, J.P.M. (eds.) ER 2019. LNCS, vol. 11788, pp. 136–152. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33223-5_12

    CrossRef  Google Scholar 

  7. Bayomie, D., Helal, I.M.A., Awad, A., Ezat, E., ElBastawissi, A.: Deducing case ids for unlabeled event logs. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 242–254. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_20

    CrossRef  Google Scholar 

  8. Burattin, A., Vigo, R.: A framework for semi-automated process instance discovery from decorative attributes. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 176–183. IEEE (2011)

    Google Scholar 

  9. Chen, P.P.S.: The entity-relationship model-toward a unified view of data. ACM Trans. Database Syst. (TODS) 1(1), 9–36 (1976)

    CrossRef  Google Scholar 

  10. 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

    CrossRef  Google Scholar 

  11. Ferreira, D.R., Gillblad, D.: Discovering process models from unlabelled event logs. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 143–158. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03848-8_11

    CrossRef  Google Scholar 

  12. Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L.A., Mark, R.: MIMIC-IV (2020). https://doi.org/10.13026/A3WN-HQ05, https://physionet.org/content/mimiciv/0.4/

  13. de Leoni, M.M., Mannhardt, F.: Road Traffic Fine Management Process (2015). 2, https://doi.org/10.4121/uuid:270fd440-1057-4fb9-89a9-b699b47990f5, https://data.4tu.nl/articles/dataset/Road_Traffic_Fine_Management_Process/12683249

  14. Li, Y., Liu, B.: A normalized levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1091–1095 (2007). https://doi.org/10.1109/TPAMI.2007.1078

    CrossRef  Google Scholar 

  15. de Murillas, E.G.L., Reijers, H.A., van der Aalst, W.M.P.: Case notion discovery and recommendation: automated event log building on databases. Knowl. Inf. Syst. 62(7), 2539–2575 (2019). https://doi.org/10.1007/s10115-019-01430-6

    CrossRef  Google Scholar 

  16. Weske, M.: Business Process Management - Concepts, Languages, Architectures, 3rd edn. Springer, Heidelberg (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tom Lichtenstein .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Lichtenstein, T., Bano, D., Weske, M. (2022). Attribute-Driven Case Notion Discovery for Unlabeled Event Logs. In: Marrella, A., Weber, B. (eds) Business Process Management Workshops. BPM 2021. Lecture Notes in Business Information Processing, vol 436. Springer, Cham. https://doi.org/10.1007/978-3-030-94343-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94343-1_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94342-4

  • Online ISBN: 978-3-030-94343-1

  • eBook Packages: Computer ScienceComputer Science (R0)