Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Unsupervised Event Abstraction
using Pattern Abstraction and
Local Process Models
Niek Tax
Felix Mannhardt
June 13th, 2017
Process Mining
SLIDE 114-6-2017
Process Mining
• Process Discovery
- “What does the process look like?”
PAGE 2
Problem: Events ≠ Activities
PAGE 3
examine
casually
records
Event Time
read master data 20:08:00
check identity 20:10:00
...
Pattern-based abstraction
PAGE 4
1. Define activity patterns based on domain knowledge
2. Define relations between activit...
Example for an activity pattern
PAGE 5
Single-entry Single-exit
• Low-level activities can be shared among patterns
• High...
Local Process Models
PAGE 6
Niek Tax, Natalia Sidorova, Reinder Haakma, and Wil M.P. van der Aalst.
“Mining local process ...
Unsupervised Abstraction Technique
PAGE 7
Experimental Results
PAGE 8
Conclusions & Future Work
• Application of LPMs as activity patterns can yield good results
• Quality of the abstraction d...
Questions?
PAGE 10
Upcoming SlideShare
Loading in …5
×

Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models

532 views

Published on

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.

Published in: Science
  • Be the first to comment

Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models

  1. 1. Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models Niek Tax Felix Mannhardt June 13th, 2017
  2. 2. Process Mining SLIDE 114-6-2017
  3. 3. Process Mining • Process Discovery - “What does the process look like?” PAGE 2
  4. 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. 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. 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. 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) …
  8. 8. Unsupervised Abstraction Technique PAGE 7
  9. 9. Experimental Results PAGE 8
  10. 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
  11. 11. Questions? PAGE 10

×