Domain-Specific Event Abstraction
Keywords:Process mining, Event abstraction, Domain knowledge, Healthcare
Process mining aims at deriving process knowledge from event logs, which contain data recorded during process executions. Typically, event logs need to be generated from process execution data, stored in different kinds of information systems. In complex domains like healthcare, data is available only at different levels of granularity. Event abstraction techniques allow the transformation of events to a common level of granularity, which enables effective process mining. Existing event abstraction techniques do not sufficiently take into account domain knowledge and, as a result, fail to deliver suitable event logs in complex application domains.
This paper presents an event abstraction method based on domain ontologies. We show that the method introduced generates semantically meaningful high-level events, suitable for process mining; it is evaluated on real-world patient treatment data of a large U.S. health system.
P. Homayounfar, “Process mining challenges in hospital information systems,” in Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE, 2012, pp. 1135–1140.
E. Rojas, J. Munoz-Gama, M. Sep´ ulveda, and D. Capurro, “Process mining in healthcare: A literature review,” Journal of Biomedical Informatics, vol. 61, pp. 224–236, 2016.
S. J. van Zelst, F. Mannhardt, M. de Leoni, and A. Koschmider, “Event abstraction in process mining: Literature review and taxonomy,” Granular Computing, pp. 1–18, 2020.
S. Sadeghianasl, A. H. M. ter Hofstede, S. Suriadi, and S. Turkay, “Collaborative and interactive detection and repair of activity labels in process vent logs,” in 2nd International Conference on Process Mining, ICPM, 2020, pp. 41–48.
X. Lu, A. Gal, and H. A. Reijers, “Discovering hierarchical processes using flexible activity trees for event abstraction,” in 2nd International Conference on Process Mining, ICPM, 2020, pp. 145–152.
K. Diba, K. Batoulis, M. Weidlich, and M. Weske, “Extraction, correlation, and abstraction of event data for process mining,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 10, no. 3, 2020.
A. Qaseem, T. J. Wilt, R. M. McLean, and M. A. Forciea, “Noninvasive treatments for acute, subacute, and chronic low back pain: A clinical practice guideline from the american college of physicians,” Annals of Internal Medicine, vol. 166, no. 7, pp. 514–530, 2017.
F. Folino, M. Guarascio, and L. Pontieri, “Mining predictive process models out of low-level multidimensional logs,” in Advanced Information Systems Engineering (CAiSE), ser. LNCS, vol. 8484, Springer, 2014, pp. 533–547.
P. H. P. Richetti, F. A. Bai˜ao, and F. M. Santoro, “Declarative process mining: Reducing discovered models complexity by pre-processing event logs,” in Business Process Management BPM, ser. LNCS, vol. 8659, Springer, 2014, pp. 400–407.
N. Tax, N. Sidorova, R. Haakma, and W. M. P. van der Aalst, “Event abstraction for process mining using supervised learning techniques,” in Proceedings of SAI Intelligent Systems Conference (IntelliSys), ser. LNNS, vol. 15, Springer, 2016.
S. J. J. Leemans, K. Goel, and S. J. van Zelst, “Using multi-level information in hierarchical process mining: Balancing behavioural quality and model complexity,” in 2nd International Conference on Process Mining, ICPM, 2020, pp. 137–144.
F. Mannhardt, M. de Leoni, H. A. Reijers, W. M. P. van der Aalst, and P. J. Toussaint, “Guided process discovery - A pattern-based approach,” Information Systems, vol. 76, pp. 1–18, 2018.
T. Baier, J. Mendling, and M. Weske, “Bridging abstraction layers in process mining,” Information Systems, vol. 46, pp. 123–139, 2014.
G. Leonardi, M. Striani, S. Quaglini, A. Cavallini, and S. Montani, “Towards semantic process mining through knowledge-based trace abstraction,” in International Symposium on Data-Driven Process Discovery and Analysis, ser. LNBIP, vol. 340, Springer, 2017, pp. 45–64.
R. Mans, W. M. P. van der Aalst, R. J. B. Vanwersch, and A. J. Moleman, “Process mining in healthcare: Data challenges when answering frequently posed questions,” in Process Support and Knowledge Representation in Health Care - BPM, Revised Selected Papers, ser. LNCS, vol. 7738, Springer, 2012, pp. 140–153.
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 International Conference on Business Process Management, ser. LNCS, Springer, 2020.
V. I. Levenshtein, “Binary Codes Capable of Correcting Deletions, Insertions and Reversals,” Soviet Physics Doklady, vol. 10, p. 707, 1966.
S. J. J. Leemans, D. Fahland, and W. M. P. van der Aalst, “Discovering block-structured process models from event logs containing infrequent behaviour,” in Business Process Management Workshops, ser. LNBIP, vol. 171, Springer, 2013, pp. 66–78.
Copyright (c) 2021 Finn Klessascheck, Tom Lichtenstein, Martin Meier, Simon Remy, Jan Philipp Sachs, Luise Pufahl, Riccardo Miotto, Erwin Boettinger, Mathias Weske
This work is licensed under a Creative Commons Attribution 4.0 International License.