Abstract
This paper presents the results of an industry expert survey about event log generation in process mining. It takes academic assumptions as a starting point and elicits practitioner’s assessments of statements about process execution, process scoping, process discovery, and process analysis. The results of the survey shed some light on challenges and perspectives around event log generation, as well as on the relationship between process models and process execution, and derive challenges for event log generation from it. The responses indicate that particularly relevant challenges exist around data integration and quality, and that process mining can benefit from a systematic integration with more traditional and wide-spread business intelligence approaches.
M. Weske—Work by this author supported by the SAP Academic Fellowship program.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Let us highlight that no open feedback that contradicts the other survey results was received.
- 2.
Here and henceforth, the number of neutral responses can – if not explicitly stated – be determined by subtracting the number of all other respondents from 32.
- 3.
This finding is to some extent confirmed by the results of another recent expert survey [8].
- 4.
To allude to the famous quote that “[e]ssentially all models are wrong, but some of them are useful”, as commonly attributed to George Box.
References
van der Aalst, W.M.P.: Extracting event data from databases to unleash process mining. In: vom Brocke, J., Schmiedel, T. (eds.) BPM - Driving Innovation in a Digital World. MP, pp. 105–128. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14430-6_8
van der Aalst, W.M.P.: Process Mining - Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
van der Aalst, W.M.P.: Object-centric process mining: dealing with divergence and convergence in event data. In: Ölveczky, P.C., Salaün, G. (eds.) SEFM 2019. LNCS, vol. 11724, pp. 3–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30446-1_1
Andrews, R., van Dun, C., Wynn, M., Kratsch, W., Röglinger, M., ter Hofstede, A.: Quality-informed semi-automated event log generation for process mining. Decis. Support Syst. 132, 113265 (2020). https://doi.org/10.1016/j.dss.2020.113265. https://www.sciencedirect.com/science/article/pii/S0167923620300208
Diba, K., Batoulis, K., Weidlich, M., Weske, M.: Extraction, correlation, and abstraction of event data for process mining. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 10(3), e1346 (2020). https://doi.org/10.1002/widm.1346
Dijkman, R., Gao, J., Syamsiyah, A., van Dongen, B., Grefen, P., ter Hofstede, A.: Enabling efficient process mining on large data sets: realizing an in-database process mining operator. Distrib. Parallel Databases 38(1), 227–253 (2020). https://doi.org/10.1007/s10619-019-07270-1
González López de Murillas, E., Reijers, H.A., van der Aalst, W.M.P.: Connecting databases with process mining: a meta model and toolset. Softw. Syst. Model. 18(2), 1209–1247 (2019). https://doi.org/10.1007/s10270-018-0664-7
Wynn, M.T., et al.: Rethinking the input for process mining: insights from the XES survey and workshop. In: Munoz-Gama, J., Lu, X. (eds.) Process Mining Workshops, pp. 3–16. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98581-3_1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Kampik, T., Weske, M. (2022). Event Log Generation: An Industry Perspective. In: Augusto, A., Gill, A., Bork, D., Nurcan, S., Reinhartz-Berger, I., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2022 2022. Lecture Notes in Business Information Processing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-031-07475-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-07475-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-07474-5
Online ISBN: 978-3-031-07475-2
eBook Packages: Computer ScienceComputer Science (R0)