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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 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
Chuprunov, M.: Auditing and GRC Automation in SAP. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35302-4
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)
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)
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
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
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)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, Amsterdam (August 2000)
Rajaraman, A., Ullman, J.: Mining of Massive Data Sets. Cambridge University Press, Cambridge (2011)
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
Berti, A.: Statistical sampling in process mining discovery. In: The 9th International Conference on Information, Process, and Knowledge Management, pp 41–43 (2017)
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)
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)
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)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
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)