Application of Process Mining in the Process of IT Incidents Management by Utilizing Kaggle’s Dataset
DOI:
https://doi.org/10.25126/jitecs.202383564Abstract
The application of process mining in Information Technology (IT) incident management has been carried out. In the face of the increasing complexity of IT systems and a surge of incidents, traditional incident management is no longer effective. This study uses Kaggle's dataset on IT incident management to analyze event logs, identify process bottlenecks, and compare process flows seen in event logs with expected flows. Process mining techniques, such as process discovery and conformance, are applied using PM4PY and Celonis tools. The analysis results with process mining produce a new model process that can be applied in practice with a conformance rate of 81%.
References
I. Park, D. Kim, J. Moon, S. Kim, Y. Kang, and S. Bae, “Searching for New Technology Acceptance Model under Social Context: Analyzing the Determinants of Acceptance of Intelligent Information Technology in Digital Transformation and Implications for the Requisites of Digital Sustainability,” Sustainability, vol. 14, no. 1, p. 579, Jan. 2022, doi: 10.3390/su14010579 (2022).
G. Sembina, K. Mayandinova, L. Naizabayeva, and S. Sagnayeva, “Development of reference incident management model,” Eastern-European Journal of Enterprise Technologies, vol. 6, no. 2 (120), pp. 41–50, Dec. 2022, doi: 10.15587/1729-4061.2022.266387 (2022).
J. Eggers, A. Hein, M. Böhm, and H. Krcmar, “No Longer Out of Sight, No Longer Out of Mind? How Organizations Engage with Process Mining-Induced Transparency to Achieve Increased Process Awareness,” Business & Information Systems Engineering, vol. 63, no. 5, pp. 491–510, Oct. 2021, doi: 10.1007/s12599-021-00715-x (2021).
B. Fazzinga, S. Flesca, F. Furfaro, and L. Pontieri, “Process Mining meets argumentation: Explainable interpretations of low-level event logs via abstract argumentation,” Inf Syst, vol. 107, p. 101987, Jul. 2022, doi: 10.1016/j.is.2022.101987 (2022).
F. M. Santoro, K. C. Revoredo, R. M. M. Costa, and T. M. Barboza, “Process Mining Techniques in Internal Auditing: A Stepwise Case Study,” iSys - Brazilian Journal of Information Systems, vol. 13, no. 4, pp. 48–76, Jul. 2020, doi: 10.5753/isys.2020.823 (2020).
S. Agostinelli, F. Covino, G. D’Agnese, C. De Crea, F. Leotta, and A. Marrella, “Supporting Governance in Healthcare Through Process Mining: A Case Study,” IEEE Access, vol. 8, pp. 186012–186025, 2020, doi: 10.1109/ACCESS.2020.3030318 (2020).
A. Bechini, A. Bondielli, P. Dell’Oglio, and F. Marcelloni, “From basic approaches to novel challenges and applications in Sequential Pattern Mining,” Electronic Research Archive, vol. 3, no. 1, pp. 44–78, 2023, doi: 10.3934/aci.2023004 (2023).
W. van der Aalst, Process Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. doi: 10.1007/978-3-662-49851-4 (2016).
H. Ponce de León, L. Nardelli, J. Carmona, and S. K. L. M. vanden Broucke, “Incorporating negative information to process discovery of complex systems,” Inf Sci (N Y), vol. 422, pp. 480–496, Jan. 2018, doi: 10.1016/j.ins.2017.09.027 (2018).
W. L. J. Lee, H. M. W. Verbeek, J. Munoz-Gama, W. M. P. van der Aalst, and M. Sepúlveda, “Recomposing conformance: Closing the circle on decomposed alignment-based conformance checking in process mining,” Inf Sci (N Y), vol. 466, pp. 55–91, Oct. 2018, doi: 10.1016/j.ins.2018.07.026 (2018).
PM4PY, “pm4py - Process Mining for Python,” 2023. https://pm4py.fit.fraunhofer.de/about-us (accessed May 23, 2023).
Angelina Prima Kurniati, Guntur Prabawa Kusuma, and Suyanto, Process Mining Sains Data Berorientasi Proses, 1st ed. Informatika Bandung, (2023).
Carolin Ullrich, Teodora Lata, and Jerome Geyer-Klingeberg, “Celonis Studio – A Low-Code Development Platform for Citizen Developers,” in Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Track at BPM 2021, CEUR Workshop Proceedings, (2021).
C. Prof. dr. ir. Wil van der Aalst Chief Scientist, Object-Centric Process Mining: The next frontier in business performance. Munich: Celonis SE, (2023).
“Process Mining for analytic-driven decision making | Kaggle.” https://www.kaggle.com/datasets/asjad99/it-incident-management-process (accessed Jul. 08, 2023).
W. Hachicha, L. Ghorbel, R. Champagnat, C. A. Zayani, and I. Amous, “Using Process Mining for Learning Resource Recommendation: A Moodle Case Study,” Procedia Comput Sci, vol. 192, pp. 853–862, 2021, doi: 10.1016/j.procs.2021.08.088 (2021).
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).