Skip to main content
SpringerLink
Log in

Constructing Decision Trees from Process Logs for Performer Recommendation

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

Included in the following conference series:

Abstract

This paper demonstrates that the discovery technique using historical event logs can be extended to predict business performance and recommend performers for running instances. For the prediction and recommendation, we adopt decision trees, which is a decision support tool in management science. Decision trees are commonly used to help identify the most likely alternative to reach a goal. To provide effective performer recommendation, we use several filters with previous performers and key tasks to the decision tree. These filters allow for a suitable recommendation according to the characteristics of the processes. The proposed approach is implemented on ProM framework and it is then evaluated through an experiment using real-life event logs, taken from a Dutch Financial Institute. The main contribution of this paper is to provide a real-time decision support tool by recommendation of the best performer for a target performance indicator during process execution based on historical data.

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 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

Institutional subscriptions

Notes

  1. 1.

    See http://www.win.tue.nl/bpi2012/doku.php?id=challenge

References

  1. van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., de Medeiros, A.K.A., Song, M., Verbeek, H.M.W.: Business process mining: an industrial application. Inform. Syst. 32(5), 713–732 (2007)

    Article  Google Scholar 

  2. de Medeiros, A.K.A., Pedrinaci, C., van der Aalst, W.M., Domingue, J., Song, M., Rozinat, A., Norton, B., Cabral, L.: An outlook on semantic business process mining and monitoring. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part II. LNCS, vol. 4806, pp. 1244–1255. Springer, Heidelberg (2007)

    Google Scholar 

  3. van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)

    Book  Google Scholar 

  4. Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.-C.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)

    Article  Google Scholar 

  5. Kang, B., Cho, N.W., Kang, S.-H.: Real-time risk measurement for business activity monitoring (BAM). Int. J. Innov. Comp. I. 5(11A), 3647–3657 (2009)

    Google Scholar 

  6. Buytendijk, F., Flint, D.: How BAM can turn a business into a real-time enterprise. Gartner Research, AV-15-4650 (2002)

    Google Scholar 

  7. Bose, R.: Understanding management data systems for enterprise performance management. Ind. Manage. Data Syst. 106(1), 43–59 (2006)

    Article  Google Scholar 

  8. van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inform. Syst. 36(2), 450–475 (2011)

    Article  Google Scholar 

  9. Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.: Supporting flexible processes through recommendations based on history. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51–66. Springer, Heidelberg (2008)

    Google Scholar 

  10. Ha, B.-H., Bae, J., Park, Y.T., Kang, S.-H.: Development of process execution rules for workload balancing on agents. Data Knowl. Eng. 56(1), 64–84 (2006)

    Article  Google Scholar 

  11. Kim, A., Jung, J.-Y.: A process mining technique for performer recommendation using decision tree. In: Korean Institute of Industrial Engineers Conference (2012)

    Google Scholar 

  12. van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H., Weijters, A., van der Aalst, W.M.: 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)

    Google Scholar 

  13. Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: ProM 6: the process mining toolkit. BPM Demonstration Track 615, 34–39 (2010)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (Nos. 2012R1A1B4003505 and 2013R1A2A2A03014718).

Author information

Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, Kyung Hee University, 1 Seochen-dong, Giheung-gu, Yongin, Gyeonggi, Republic of Korea

    Aekyung Kim, Josue Obregon & Jae-Yoon Jung

Authors
  1. Aekyung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Josue Obregon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jae-Yoon Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jae-Yoon Jung .

Editor information

Editors and Affiliations

  1. University of Rostock, Rostock, Germany

    Niels Lohmann

  2. Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South Korea)

    Minseok Song

  3. Stockholm University, Stockholm, Sweden

    Petia Wohed

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Kim, A., Obregon, J., Jung, JY. (2014). Constructing Decision Trees from Process Logs for Performer Recommendation. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-06257-0_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation