Skip to main content
Book cover

International Conference on Business Process Management

BPM 2021: Business Process Management Forum pp 263–279Cite as

Generating High Quality Samples of Process Cases in Internal Audit

  • 562 Accesses

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 427)

Abstract

Business Process Mining (BPM) has become an essential tool in internal audit (IA), which helps auditors analyze potential risks in clients’ core business processes. After finishing the risk analysis task for the target business process with BPM, auditors need to sample a small set of representative process cases from event log, based on which clients will verify the analysis results and analyze the triggers for the risks in the target business process. This process case sampling (PCS) step is important because it is difficult to check each single case from a large event log. Therefore, the quality of the set of case samples (SCS) from PCS is regarded as one of the success factors in IA project. Manual PCS and simple random PCS are two basic methods for executing PCS. However, both methods cannot assure the quality of the generated SCS. In this paper, we propose an advanced PCS method. It first defines the risk of process cases as well as the factors that affect the quality of SCS, before dynamically optimizing the quality of SCS during PCS. Our experimental evaluation highlights that our approach yields higher quality SCS than manual PCS and simple random PCS.

Keywords

  • Business process mining
  • Business process management
  • Process case sampling
  • Process risk management
  • Internal auditing

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-85440-9_16
  • Chapter length: 17 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   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-85440-9
  • 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   84.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    As introduced in Sect. 2, risk-diversity is affected by both risk-level and risk-dissimilarity. Therefore, QS indirectly takes risk-diversity into account.

References

  1. Chuprunov, M.: Auditing and GRC Automation in SAP. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35302-4

    CrossRef  Google Scholar 

  2. Seeliger, A., Nolle, T., Mühlhäuser, M.: Process explorer: an interactive visual recommendation system for process mining. In: KDD Workshop on Interactive Data Exploration and Analytics (2018)

    Google Scholar 

  3. Fricker, R.D.: Sampling methods for web and e-mail surveys. In: Fielding, N., Lee, R.M., Blank G. (eds.) The Sage Handbook of Online Research Methods, pp. 195–216. Los Angeles, CA: Sage (2008)

    Google Scholar 

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

    CrossRef  Google Scholar 

  5. 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

    CrossRef  Google Scholar 

  6. Niwattanakul, S., Singthongchai, J., Naenudorn, E., Wanapu, S.: Using of Jaccard coefficient for keywords similarity. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 1. pp. 1–5 (2013)

    Google Scholar 

  7. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Amsterdam (August 2000)

    Google Scholar 

  8. Rajaraman, A., Ullman, J.: Mining of Massive Data Sets. Cambridge University Press, Cambridge (2011)

    CrossRef  Google Scholar 

  9. Carmona, J., Cortadella, J.: Process mining meets abstract interpretation. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 184–199. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15880-3_18

    CrossRef  Google Scholar 

  10. Berti, A.: Statistical sampling in process mining discovery. In: The 9th International Conference on Information, Process, and Knowledge Management, pp 41–43 (2017)

    Google Scholar 

  11. Bauer, M., Senderovich, A., Gal, A., Grunske, L., Weidlich, M.: How much event data is enough? a statistical framework for process discovery. In: CAiSE, pp. 239–256 (2018)

    Google Scholar 

  12. Fani Sani, F., van Zelst, S.J., van der Aalst, W.M.P.: Improving the performance of process discovery algorithms by instance selection. Comput. Sci. Inf. Syst. 17(3), 927–958 (2020)

    CrossRef  Google Scholar 

  13. Fani Sani, F., van Zelst, S.J., van der Aalst, W.M.P.: The impact of biased sampling of event logs on the performance of process discovery. In: Computing (2021)

    Google Scholar 

  14. Bauer, M., van der Aa, H., Weidlich, M.: Estimating process conformance by trace sampling and result approximation. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 179–197. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_13

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. KPMG AG Wirtschaftsprüfungsgesellschaft, Stuttgart, Germany

    Yaguang Sun

  2. Karlsruhe University of Applied Sciences, Karlsruhe, Germany

    Lyth AI-Khazrage

  3. University of Mannheim, Mannheim, Germany

    Ömer Özümerzifon

Authors
  1. Yaguang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lyth AI-Khazrage
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ömer Özümerzifon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaguang Sun .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Carlton, VIC, Australia

    Dr. Artem Polyvyanyy

  2. NICTA Queensland Res Lab, Queensland University of Technology, Brisbane, QLD, Australia

    Dr. Moe Thandar Wynn

  3. EB24, Ghent University, Ghent, Belgium

    Dr. Amy Van Looy

  4. University of Ulm, Ulm, Germany

    Manfred Reichert

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Sun, Y., AI-Khazrage, L., Özümerzifon, Ö. (2021). Generating High Quality Samples of Process Cases in Internal Audit. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management Forum. BPM 2021. Lecture Notes in Business Information Processing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-030-85440-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85440-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85439-3

  • Online ISBN: 978-3-030-85440-9

  • eBook Packages: Computer ScienceComputer Science (R0)