Database-Less Extraction of Event Logs from Redo Logs
DOI:
https://doi.org/10.52825/bis.v1i.66Keywords:
Event Log, Redo Log, Database Schema, Log Extraction, Process MiningAbstract
Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.
Downloads
References
W. M. P. van der Aalst, Process Mining - Data Science in Action, Second Edition. Springer, 2016, ISBN: 978-3-662-49850-7. DOI: 10.1007/978-3-662-49851-4. [Online]. Available: https://doi.org/10.1007/978-3-662-49851-4.
S. Remy, L. Pufahl, J.-P. Sachs, E. P. B¨ ottinger, and M. Weske, “Event log generation in a health system: A case study,” in Business Process Management - 18th International Conference, BPM 2020, Seville, Spain, September 13-18, 2020, Proceedings, D. Fahland, C. Ghidini, J. Becker, and M. Dumas, Eds., ser. Lecture Notes in Computer Science, vol. 12168, Springer, 2020, pp. 505–522. DOI: 10.1007/978-3-030-58666-9_29. [Online].
E. G. L. de Murillas, G. E. Hoogendoorn, and H. A. Reijers, “Redo log process mining in real life: Data challenges & opportunities,” in Business Process ManagementWorkshops - BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers, ser. Lecture Notes in Business Information Processing, vol. 308, Springer, 2017, pp. 573–587. DOI: 10 . 1007 / 978 - 3 - 319 - 74030 - 0 _45. [Online]. Available: https://doi.org/10.1007/978-3-319-74030-0%5C_45.
W. M. P. van der Aalst, “Extracting event data from databases to unleash process mining,” in BPM - Driving Innovation in a Digital World, J. vom Brocke and T. Schmiedel, Eds., Springer, 2015, pp. 105–128. DOI: 10.1007/978-3-319-14430-6_8. [Online]. Available: https://doi.org/10.1007/978-3-319-14430-6%5C_8.
E. Gonz´alez-L´opez de Murillas, W. van der Aalst, and H. Reijers, “Process mining on databases: Unearthing historical data from redo logs,” English, Lecture Notes in Computer Science, no. 9253, pp. 367–385, 2015, ISSN: 0302-9743. DOI: 10.1007/978- 3-319-23063-4_25.
K. Diba, K. Batoulis, M. Weidlich, and M. Weske, “Extraction, correlation, and abstraction of event data for process mining,” WIREs Data Mining and Knowledge Discovery, vol. 10, no. 3, e1346, 2020. DOI: 10.1002/widm.1346. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1346. [Online]. Available: https://onlinelibrary.wiley. com/doi/abs/10.1002/widm.1346.
L. Jiang and F. Naumann, “Holistic primary key and foreign key detection,” J. Intell. Inf. Syst., vol. 54, no. 3, pp. 439–461, 2020. DOI: 10.1007/s10844-019-00562-z. [Online]. Available: https://doi.org/10.1007/s10844-019-00562-z.
A. Rostin, O. Albrecht, J. Bauckmann, F. Naumann, and U. Leser, “A machine learning approach to foreign key discovery,” 2009.
A. E. Johnson, T. J. Pollard, L. Shen, L.-w. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. A. Celi, and R. G. Mark, “Mimic-iii, a freely accessible critical care database,” Scientific data, vol. 3, p. 160 035, 2016.
S. J. J. Leemans, D. Fahland, and W. van der Aalst, “Process and deviation exploration with inductive visual miner,” in Proceedings of the BPM Demo Sessions Co-located with the 12th International Conference on Business Process Management (BPM), ser. CEUR Workshop Proceedings, vol. 1295, CEUR-WS.org, 2014, p. 46.
B. F. van Dongen, A. K. A. de Medeiros, H. M. W. Verbeek, A. J. M. M. Weijters, and W. van der Aalst, “The ProM Framework: A New Era in Process Mining Tool Support,” in Applications and Theory of Petri Nets 2005, 26th International Conference, ICATPN, ser. Lecture Notes in Computer Science, vol. 3536, Springer, 2005, pp. 444–454. [Online]. Available: https://doi.org/10.1007/11494744%5C_25.
Downloads
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2021 Dorina Bano, Tom Lichtenstein, Finn Klessascheck, Mathias Weske
This work is licensed under a Creative Commons Attribution 4.0 International License.
