Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
Research-in-progress presented at BPMDS 2017: http://ceur-ws.org/Vol-1859/bpmds-06-paper.pdf F. Mannhardt, N. Tax (2017). Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models. In BPMDS’2017 RADAR proceedings, pp. 55–63. Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.
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Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
- 1. Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models Niek Tax Felix Mannhardt June 13th, 2017
- 3. Process Mining • Process Discovery - “What does the process look like?” PAGE 2
- 4. Problem: Events ≠ Activities PAGE 3 examine casually records Event Time read master data 20:08:00 check identity 20:10:00 check balance 20:16:00 Event Time read barcode 20:11:00 read master data 20:12:00 check revocation 20:25:00 records check ticket
- 5. Pattern-based abstraction PAGE 4 1. Define activity patterns based on domain knowledge 2. Define relations between activities based on domain knowledge 3. Map event-level event log to activity-level event log using alignments Problem: domain knowledge might not be available! Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers, Wil M.P. van der Aalst, and Pieter J. Toussaint. "From low-level events to activities-a pattern-based approach." In International Conference on Business Process Management, pp. 125-141. Springer International Publishing, 2016.
- 6. Example for an activity pattern PAGE 5 Single-entry Single-exit • Low-level activities can be shared among patterns • High-level activities can be executed in parallel • Noise in the low-level event log is handled
- 7. Local Process Models PAGE 6 Niek Tax, Natalia Sidorova, Reinder Haakma, and Wil M.P. van der Aalst. “Mining local process models”. Journal of Innovation in Digital Ecosystems, 3(2), pp.183-196, Elsevier, 2016. Ranking of process models 1) 2) 3) …
- 10. Conclusions & Future Work • Application of LPMs as activity patterns can yield good results • Quality of the abstraction dependent on - Number of LPMs used - Diversity threshold (i.e., which LPMs are used) - Composition method of the abstraction technique • Research on the interplay between parameters and result needed! • Automatic parameter selection possible? • Semi–supervised method: - Propose a set of LPMs that is likely to improve the event log - Let the user make the final decision PAGE 9
Editor's Notes
- Process discovery is the task of discovering a process model from a log of events, extracted from for instance an ERP system. Events in an event log contain a case, which groups together events that somehow belong together, like here where each case represents a paper and each event represents a step in the submission process of this paper. Each case can be seen as an instance of the process. The process model generated by a process discovery algorithm can be in any process modeling notation depending on the algorithm, often Petri nets are used, like here on the screen.