Skip to main content
SpringerLink
Log in

Towards Understanding the Role of the Human in Event Log Extraction

  • Conference paper
  • First Online:
Book cover Business Process Management Workshops (BPM 2021)

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

Included in the following conference series:

Abstract

Process mining is widely used to visualize, analyze, and improve business processes. However, often its application is hindered by the considerable preparation effort that needs to be conducted by humans. One of the key tasks required in this context is obtaining the input artifact for process mining techniques: the event log. The data that is required for building such an event log typically needs to be collected from several databases and then transformed into a suitable format. While it has become clear to both academics and practitioners that the amount of human work is substantial, there is no deep understanding of the exact activities humans need to perform. Therefore, we use this paper to develop a precise understanding of how humans are involved in event log extraction. Based on a structured literature review and qualitative data coding, we derive a taxonomy of human tasks in event log extraction. This taxonomy can serve as input for both future automation efforts, as well as for process mining methodologies.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

References

  1. van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. van der Aalst, W.M.P., Guo, S., Gorissen, P.: Comparative process mining in education: an approach based on process cubes. In: Ceravolo, P., Accorsi, R., Cudre-Mauroux, P. (eds.) SIMPDA 2013. LNBIP, vol. 203, pp. 110–134. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46436-6_6

    Chapter  Google Scholar 

  3. Andrews, R., van Dun, C.G.J., Wynn, M.T., Kratsch, W., Röglinger, M.K.E., ter Hofstede, A.H.M.: Quality-informed semi-automated event log generation for process mining. Decis. Support Syst. 132, 113265 (2020)

    Article  Google Scholar 

  4. Andrews, R., et al.: Leveraging data quality to better prepare for process mining: an approach illustrated through analysing road trauma pre-hospital retrieval and transport processes in Queensland. Int. J. Environ. Res. Public Health 16(7), 1138 (2019)

    Article  Google Scholar 

  5. Andrews, R., et al.: Pre-hospital retrieval and transport of road trauma patients in Queensland. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 199–213. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11641-5_16

    Chapter  Google Scholar 

  6. Benevento, E., Dixit, P.M., Sani, M.F., Aloini, D., van der Aalst, W.M.P.: Evaluating the effectiveness of interactive process discovery in healthcare: a case study. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 508–519. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_41

    Chapter  Google Scholar 

  7. Calvanese, D., Montali, M., Syamsiyah, A., van der Aalst, W.M.P.: Ontology-driven extraction of event logs from relational databases. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 140–153. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_12

    Chapter  Google Scholar 

  8. Cho, M., Song, M., Yoo, S.: A systematic methodology for outpatient process analysis based on process mining. In: Ouyang, C., Jung, J.-Y. (eds.) AP-BPM 2014. LNBIP, vol. 181, pp. 31–42. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08222-6_3

    Chapter  Google Scholar 

  9. de Murillas, E.G.L., van der Aalst, W.M.P., Reijers, H.A.: Process mining on databases: unearthing historical data from redo logs. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 367–385. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23063-4_25

    Chapter  Google Scholar 

  10. De Weerdt, J., Schupp, A., Vanderloock, A., Baesens, B.: Process mining for the multi-faceted analysis of business processes - a case study in a financial services organization. Comput. Ind. 64(1), 57–67 (2013)

    Article  Google Scholar 

  11. Deeva, G., De Smedt, J., De Koninck, P., De Weerdt, J.: Dropout prediction in MOOCs: a comparison between process and sequence mining. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 243–255. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_18

    Chapter  Google Scholar 

  12. Diba, K., Batoulis, K., Weidlich, M., Weske, M.: Extraction, correlation, and abstraction of event data for process mining. WIREs Data Min. Knowl. Discov. 10(3), e1346 (2020)

    Google Scholar 

  13. van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM\(^2\): a process mining project methodology. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_19

    Chapter  Google Scholar 

  14. Epure, E.V., Hug, C., Deneckére, R., Brinkkemper, S.: What shall I do next? In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 473–487. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_32

    Chapter  Google Scholar 

  15. Er, M., Arsad, N., Astuti, H.M., Kusumawardani, R.P., Utami, R.A.: Analysis of production planning in a global manufacturing company with process mining. J. Enterp. Inf. Manag. 31(2), 317–337 (2018)

    Article  Google Scholar 

  16. Flath, C.M., Stein, N.: Towards a data science toolbox for industrial analytics applications. Comput. Ind. 94, 16–25 (2018)

    Article  Google Scholar 

  17. dos Santos Garcia, C., Meincheim, A., Garcia Filho, F.C., Santos, E.A.P., Scalabrin, E.E.: Getting insights to improve business processes with agility: a case study using process mining. In: ICSMC, pp. 1336–1343. IEEE (2019)

    Google Scholar 

  18. Grisold, T., Mendling, J., Otto, M., vom Brocke, J.: Adoption, use and management of process mining in practice. Bus. Process. Manag. J. 27(2), 369–387 (2020)

    Article  Google Scholar 

  19. Gunnarsson, B.R., vanden Broucke, S.K.L.M., De Weerdt, J.: Predictive process monitoring in operational logistics: a case study in aviation. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 250–262. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_21

    Chapter  Google Scholar 

  20. Jans, M., Alles, M., Vasarhelyi, M.: The case for process mining in auditing: sources of value added and areas of application. Int. J. Account. Inf. Syst. 14(1), 1–20 (2013)

    Article  Google Scholar 

  21. Jareevongpiboon, W., Janecek, P.: Ontological approach to enhance results of business process mining and analysis. Bus. Process. Manag. J. 19(3), 459–476 (2013)

    Article  Google Scholar 

  22. Johnson, O.A., Ba Dhafari, T., Kurniati, A., Fox, F., Rojas, E.: The clearpath method for care pathway process mining and simulation. In: Daniel, F., Sheng, Q.Z., Motahari, H. (eds.) BPM 2018. LNBIP, vol. 342, pp. 239–250. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11641-5_19

    Chapter  Google Scholar 

  23. Knoll, D., Reinhart, G., Prüglmeier, M.: Enabling value stream mapping for internal logistics using multidimensional process mining. Expert Syst. Appl. 124, 130–142 (2019)

    Article  Google Scholar 

  24. Lemos, A.M., Sabino, C.C., Lima, R.M.F., Oliveira, C.A.L.: Using process mining in software development process management: a case study. In: ICSMC. IEEE (2011)

    Google Scholar 

  25. Mans, R.S., Schonenberg, M.H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of process mining in healthcare – a case study in a Dutch hospital. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 425–438. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-92219-3_32

    Chapter  Google Scholar 

  26. Marazza, F., et al.: Comparing process models for patient populations: application in breast cancer care. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 496–507. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_40

    Chapter  Google Scholar 

  27. Nooijen, E.H.J., van Dongen, B.F., Fahland, D.: Automatic discovery of data-centric and artifact-centric processes. In: La Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 316–327. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_36

    Chapter  Google Scholar 

  28. Park, M., Song, M., Baek, T.H., Son, S.Y., Ha, S.J., Cho, S.W.: Workload and delay analysis in manufacturing process using process mining. In: Bae, J., Suriadi, S., Wen, L. (eds.) AP-BPM 2015. LNBIP, vol. 219, pp. 138–151. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19509-4_11

    Chapter  Google Scholar 

  29. Partington, A., Wynn, M.T., Suriadi, S., Ouyang, C., Karnon, J.: Process mining for clinical processes: a comparative analysis of four Australian hospitals. ACM TMIS 5(4), 1–18 (2015)

    Article  Google Scholar 

  30. Potavin, J., Jongswat, N., Premchaiswadi, W.: Applying fuzzy-genetic mining in conformance and dependency relations. In: ICT & KE, pp. 228–235. IEEE (2012)

    Google Scholar 

  31. Rojas, E., Cifuentes, A., Burattin, A., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Performance analysis of emergency room episodes through process mining. Int. J. Environ. Res. Public 16(7), 251–263 (2019)

    Google Scholar 

  32. Rojas, E., Munoz-Gama, J., Sepúlveda, M., Capurro, D.: Process mining in healthcare: a literature review. J. Biomed. Inform. 61, 224–236 (2016)

    Article  Google Scholar 

  33. Saldaña, J.: The Coding Manual for Qualitative Researchers. Sage, Thousand Oaks (2009)

    Google Scholar 

  34. Suriadi, S., Wynn, M.T., Ouyang, C., ter Hofstede, A.H.M., van Dijk, N.J.: Understanding process behaviours in a large insurance company in Australia: a case study. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 449–464. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38709-8_29

    Chapter  Google Scholar 

  35. 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 

  36. Wang, Y., Caron, F., Vanthienen, J., Huang, L., Guo, Y.: Acquiring logistics process intelligence: methodology and an application for a Chinese bulk port. Expert Syst. Appl. 41(1), 195–209 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672.

Author information

Authors and Affiliations

  1. Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Vinicius Stein Dani, Jan Martijn E. M. van der Werf, Xixi Lu, Iris Beerepoot, Jelmer J. Koorn & Hajo A. Reijers

  2. Kühne Logistics University, Großer Grasbrook 17, 20457, Hamburg, Germany

    Henrik Leopold

Authors
  1. Vinicius Stein Dani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henrik Leopold
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Martijn E. M. van der Werf
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xixi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iris Beerepoot
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jelmer J. Koorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hajo A. Reijers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinicius Stein Dani .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Andrea Marrella

  2. University of St. Gallen, St. Gallen, Switzerland

    Barbara Weber

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stein Dani, V. et al. (2022). Towards Understanding the Role of the Human in Event Log Extraction. 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_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94343-1_7

  • 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)

Publish with us

Policies and ethics

Search

Navigation