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From Low-Level Events to Activities
A Pattern-based Approach
Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers,
Wil ...
Scope: Analysis of event data
PAGE 1
Problem: Events ≠ Recognizable activities
PAGE 2
Alarm
records
Event Time
Red Button 20:08:00
Green Button 20:10:00
Gray B...
Goal 1: From low-level to high-level events (Supervised)
PAGE 3
Event Log Abstracted Log
Low-level Event Time
Red Button 2...
Goal 2: Deal with shared labels, concurrency and noise
PAGE 4
Low-level Event Time
Red Button 20:08:00
Green Button 20:10:...
Related work
• Unsupervised event abstraction (Ferreira et al., Folino et al., …)
• Does not take domain knowledge into ac...
Overview: From Low-level Events to Activities
PAGE 6
Event Log Aligned Log Abstracted Log
Activity
Pattern
Abstraction
Mod...
1) Encode knowledge on activities as activity patterns
PAGE 7
Alarm Handover
GreenV
GrayV
Pattern defines traces expected ...
What about interaction between activity patterns?
PAGE 8
Alarm
Interaction?
GreenV
GrayV
Visit
From Low-level Events to Activities
PAGE 9
Event Log Aligned Log Abstracted Log
Activity
Pattern
Abstraction
Model
2) Comp...
2) Build an integrated abstraction model
PAGE 10
Interaction?
Alarm Visit
Parallel
Alarm Visit
Interleaving
↔
Alarm Visit
...
2) Build an integrated abstraction model
PAGE 11
Handover
Alarm Visit
↔
0..✱
0..✱
0..✱
0..✱
Automatic
compilation
Abstract...
From Low-level Events to Activities
PAGE 12
Event Log Aligned Log Abstracted Log
Activity
Pattern
Abstraction
Model
3) Ali...
3) Align event log to abstraction model
PAGE 13
Event Log
Low-level Event Time
Red Button 20:08:00
Green Button 20:10:00
G...
3) Align event log to abstraction model
PAGE 14
Low-level Event Time
Red Button 20:08:00
Green Button 20:10:00
Green Butto...
From Low-level Events to Activities
PAGE 15
Event Log Aligned Log Abstracted Log
Activity
Pattern
Abstraction
Model
4) Abs...
4) Create the abstracted event log with high-level events
PAGE 16
Aligned Log
Abstract aligned events
Abstracted Log
High-...
Recap: Abstraction Method
PAGE 17
Event Log Aligned Log Abstracted Log
Activity
Pattern
Abstraction
Model
3) Align event l...
Evaluation: Digital whiteboard system in a hospital
PAGE 18
• Information system
• Digital whiteboard
• Supports work of n...
Evaluation: Activity Patterns
PAGE 19
• 18 activity patterns
• Our assumptions
• Interview with expert
• Abstraction model...
Evaluation: Detected shift change pattern
PAGE 20
Shift pattern captures
a meaningful activity
Blue: Nurse Changed
Green: ...
Conclusion & Future work
PAGE 21
• Method
• Pattern-based event abstraction
• Knowledge encoded as activity patterns
• Abs...
Questions?
PAGE 22
@fmannhardt - f.mannhardt@tue.nl
http://promtools.org - LogEnhancement package
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From Low-Level Events to Activities - A Pattern based Approach

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Presentation at BPM'16 on our paper 'From Low-Level Events to Activities - A Pattern based Approach'. Published at dx.doi.org/10.1007/978-3-319-45348-4_8

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From Low-Level Events to Activities - A Pattern based Approach

  1. 1. From Low-Level Events to Activities A Pattern-based Approach Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers, Wil M.P. van der Aalst, Pieter J. Toussaint (NTNU, Trondheim) Paper: 10.1007/978-3-319-45348-4_8
  2. 2. Scope: Analysis of event data PAGE 1
  3. 3. Problem: Events ≠ Recognizable activities PAGE 2 Alarm records Event Time Red Button 20:08:00 Green Button 20:10:00 Gray Button 20:16:00 Handover Event Time Nurse Changed 07:02:00 Green Button 07:15:00 Gray Button 07:18:00 records Event Time Green Button 22:02:00 Gray Button 22:10:00 records Visit
  4. 4. Goal 1: From low-level to high-level events (Supervised) PAGE 3 Event Log Abstracted Log Low-level Event Time Red Button 20:08:00 Green Button 20:10:00 Gray Button 20:16:00 Green Button 22:02:00 Gray Button 22:10:00 Nurse Changed 07:02:00 Green Button 07:15:00 Gray Button 07:18:00 High-level Event Start Complete Alarm 20:08:00 20:16:00 Visit 22:02:00 22:10:00 Shift Change 07:02:00 07:18:00
  5. 5. Goal 2: Deal with shared labels, concurrency and noise PAGE 4 Low-level Event Time Red Button 20:08:00 Green Button 20:10:00 Gray Button 20:16:00 Green Button 22:02:00 Nurse Changed 22:09:00 Gray Button 22:10:00 Nurse Changed 22:15:00 Nurse Changed 07:02:00 Green Button 07:15:00 Gray Button 07:18:00 High-level Event Start Complete Alarm 20:08:00 20:10:00 Visit 22:02:00 22:10:00 Handover 07:02:00 07:18:00 Missing events Unexpected events Event Log Abstracted Log
  6. 6. Related work • Unsupervised event abstraction (Ferreira et al., Folino et al., …) • Does not take domain knowledge into account • Supervised event abstraction (Thomas Baier et al., …) • Assumes knowledge of a complete process model • Semi-automatic discovery of the mapping between events and activities • Uses clustering and constraint satisfaction to determine the mapping • Complex event processing • Focus on detection of event patterns in data streams • No concept of process instance / trace PAGE 5
  7. 7. Overview: From Low-level Events to Activities PAGE 6 Event Log Aligned Log Abstracted Log Activity Pattern Abstraction Model 3) Align model & log 4) Abstract events 1) Encode knowledge 2) Compose model
  8. 8. 1) Encode knowledge on activities as activity patterns PAGE 7 Alarm Handover GreenV GrayV Pattern defines traces expected for one activity instance! Data Petri net used for clear semantics! Visit
  9. 9. What about interaction between activity patterns? PAGE 8 Alarm Interaction? GreenV GrayV Visit
  10. 10. From Low-level Events to Activities PAGE 9 Event Log Aligned Log Abstracted Log Activity Pattern Abstraction Model 2) Compose model
  11. 11. 2) Build an integrated abstraction model PAGE 10 Interaction? Alarm Visit Parallel Alarm Visit Interleaving ↔ Alarm Visit Choice ✖ Alarm Visit Sequence Alarm Visit Alarm 0..✱ Repetition
  12. 12. 2) Build an integrated abstraction model PAGE 11 Handover Alarm Visit ↔ 0..✱ 0..✱ 0..✱ 0..✱ Automatic compilation Abstraction Model Compiled Abstraction Model (DPN)
  13. 13. From Low-level Events to Activities PAGE 12 Event Log Aligned Log Abstracted Log Activity Pattern Abstraction Model 3) Align model & log
  14. 14. 3) Align event log to abstraction model PAGE 13 Event Log Low-level Event Time Red Button 20:08:00 Green Button 20:10:00 Green Button 22:02:00 Nurse Changed 22:09:00 Gray Button 22:10:00 Nurse Changed 22:15:00 Nurse Changed 07:02:00 Green Button 07:15:00 Compiled abstraction model Alignment
  15. 15. 3) Align event log to abstraction model PAGE 14 Low-level Event Time Red Button 20:08:00 Green Button 20:10:00 Green Button 22:02:00 Nurse Changed 22:09:00 Gray Button 22:10:00 Nurse Changed 22:15:00 Nurse Changed 07:02:00 Green Button 07:15:00 Process Step Time RedA 20:08:00 GreenA 20:10:00 GrayA GreenV 22:02:00 GrayV 22:10:00 NurseS 07:02:00 GreenS 07:15:00 Event Log Existing alignment methods Aligned Log Model only Log only …
  16. 16. From Low-level Events to Activities PAGE 15 Event Log Aligned Log Abstracted Log Activity Pattern Abstraction Model 4) Abstract events
  17. 17. 4) Create the abstracted event log with high-level events PAGE 16 Aligned Log Abstract aligned events Abstracted Log High-level Event Transition Time Alarm start 20:08:00 Alarm complete 20:10:00 Visit start 22:02:00 Visit complete 22:10:00 Handover start 07:02:00 Handover complete 07:18:00 Process Step Time RedA 20:08:00 GreenA 20:10:00 GrayA GreenV 22:02:00 GrayV 22:10:00 NurseS 07:02:00 GreenS 07:15:00 GrayS 07:18:00 Alarm Visit Handover Use time of previous mapped event.
  18. 18. Recap: Abstraction Method PAGE 17 Event Log Aligned Log Abstracted Log Activity Pattern Abstraction Model 3) Align event log to abstraction model 4) Abstract based on aligned log 1) Encode knowledge on activities as patterns 2) Build integrated model
  19. 19. Evaluation: Digital whiteboard system in a hospital PAGE 18 • Information system • Digital whiteboard • Supports work of nurses • Mixed clinical & logistic info • Flexible system • Dataset • One year • > 8,000 cases • > 280,000 events • Event per changed cell
  20. 20. Evaluation: Activity Patterns PAGE 19 • 18 activity patterns • Our assumptions • Interview with expert • Abstraction model • Most interleaved & repeated • Five concurrent activities • Resulting abstraction • Low average error rate • Successful abstraction
  21. 21. Evaluation: Detected shift change pattern PAGE 20 Shift pattern captures a meaningful activity Blue: Nurse Changed Green: Call Signal Green Yellow: Call Signal Gray Relatively rare pattern! 00:00 24:00 00:00 24:00
  22. 22. Conclusion & Future work PAGE 21 • Method • Pattern-based event abstraction • Knowledge encoded as activity patterns • Abstraction using alignment methods • Results • Handles shared labels, concurrency and noise • Alignment gives reliability measure • Successfully used in a case study • Future work • In-depth comparison to related methods • Prioritization among activity patterns • Decomposed of alignment methods Implemented in ProM 6.6
  23. 23. Questions? PAGE 22 @fmannhardt - f.mannhardt@tue.nl http://promtools.org - LogEnhancement package

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