Skip to main content

Deducing Case IDs for Unlabeled Event Logs

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 256))

Abstract

Event logs are invaluable sources of knowledge about the actual execution of processes. A large number of techniques to mine, check conformance and analyze performance have been developed based on logs. All these techniques require at least case ID, activity ID and the timestamp to be in the log. If one of those is missing, these techniques cannot be applied. Real life logs are rarely originating from a centrally orchestrated process execution. Thus, case ID might be missing, known as unlabeled log. This requires a manual preprocessing of the log to assign case ID to events in the log.

In this paper, we propose a new approach to deduce case ID for the unlabeled event log depending on the knowledge about the process model. We provide a set of labeled logs instead of a single labeled log with different rankings. We evaluate our prototypical implementation against similar approaches.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    Complete implementation in https://github.com/DinaBayomie/DeducingCaseId.

References

  1. der Aalst, W.V.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)

    Book  MATH  Google Scholar 

  2. van der Aalst, W.M.P., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM Workshops 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  3. Bayomie, D., Helal, I.M.A., Awad, A., Ezat, E., ElBastawissi, A.: Deducing Case IDs for unlabeled Event Logs. Technical report, Cairo University. http://scholar.cu.edu.eg/?q=ahmedawad/files/bplabellingeventlog.pdf

  4. Doganata, Y.N.: Designing internal control points in partially managed processes by using business vocabulary. In: ICDE Workshops. pp. 267–272. IEEE (2011)

    Google Scholar 

  5. Doganata, Y., Curbera, F.: Effect of using automated auditing tools on detecting compliance failures in unmanaged processes. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 310–326. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  6. Dustdar, S., Gombotz, R.: Discovering web service workflows using web services interaction mining. Int. J. Bus. Process Integr. Manag. 1(4), 256 (2006)

    Article  Google Scholar 

  7. 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)

    Chapter  Google Scholar 

  8. Herzberg, N., Kunze, M., Rogge-Solti, A.: Towards process evaluation in non-automated process execution environments. In: ZEUS. CEUR Workshop Proceedings, vol. 847, pp. 97–103 (2012). www.CEUR-WS.org

  9. Idika, N.C., Varia, M., Phan, H.: The probabilistic provenance graph. In: IEEE Symposium on Security and Privacy Workshops. pp. 34–41. IEEE Computer Society (2013)

    Google Scholar 

  10. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) Business Process Management Workshops. LNBIP, vol. 171, pp. 66–78. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  11. Mukhi, N.K.: Monitoring unmanaged business processes. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 44–59. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. Polyvyanyy, A., Weidlich, M.: Towards a compendium of process technologies - the jBPT library for process model analysis. In: CEUR Workshop Proceedings onCAiSE 2013 Forum, vol. 998, pp. 106–113 (2013). www.CEUR-WS.org

  13. Rogge-Solti, A.: Probabilistic Estimation of Unobservered Process Events. University of Potsdam, Ph.D. (2014)

    Google Scholar 

  14. Rogge-Solti, A., Mans, R.S., van der Aalst, W.M.P., Weske, M.: Repairing event logs using timed process models. In: Demey, Y.T., Panetto, H. (eds.) OTM 2013 Workshops 2013. LNCS, vol. 8186, pp. 705–708. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  15. Suriadi, S., Ouyang, C., van der Aalst, W.M., ter Hofstede, A.H.: Event Gap Analysis: Understanding Why Processes Take Time. Technical report QUT: ePrints (2014)

    Google Scholar 

  16. Van Der Aalst, W.M.P., Van Dongen, B.F., Günther, C., Rozinat, A., Verbeek, H.M.W., Weijters, A.: Prom: the process mining toolkit. In: CEUR Workshop Proceedings, vol. 489 (2009)

    Google Scholar 

  17. Walicki, M., Ferreira, D.R.: Mining sequences for patterns with non-repeating symbols. In: IEEE Congress on Evolutionary Computation, CEC. pp. 1–8. IEEE (2010)

    Google Scholar 

  18. Walicki, M., Ferreira, D.R.: Sequence partitioning for process mining with unlabeled event logs. Data Knowl. Eng. 70(10), 821–841 (2011)

    Article  Google Scholar 

  19. Walpole, E.R., Myers, R.H., Myers, S.L., Ye, K.E.: Probability and Statistics for Engineers and Scientists, 9th edn. Pearson, London (2011)

    MATH  Google Scholar 

  20. Weidlich, M.: Behavioral profiles - a relational approach to behaviour consistency. Ph.D. thesis, University of Potsdam (2011)

    Google Scholar 

  21. Weidlich, M., Polyvyanyy, A., Mendling, J., Weske, M.: Causal behavioural profiles - efficient computation, applications, and evaluation. Fundamenta Informaticae 113, 399–435 (2011)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Dina Bayomie or Iman M. A. Helal .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Bayomie, D., Helal, I.M.A., Awad, A., Ezat, E., ElBastawissi, A. (2016). Deducing Case IDs for Unlabeled Event Logs. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42887-1_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42886-4

  • Online ISBN: 978-3-319-42887-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics