Process Mining Reloaded: Event Structures as a Unified Representation of Process Models and Event Logs
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Keynote talk at the 36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2015). Screencast available at: https://youtu.be/9bQr0r_WaoE
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Process Mining Reloaded: Event Structures as a Unified Representation of Process Models and Event Logs
- 1. Marlon Dumas University of Tartu, Estonia Petri Nets 2015 | Brussels | 24 June 2015
- 2. Process Mining / event log discovered model Discovery Conformance Deviance Difference diagnostics Performanc e input model Enhanced model event log’ 2
- 3. Automated Process Discovery 3 Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility CID Task Time Stamp … 13219 Enter Loan Application 2007-11-09 T 11:20:10 - 13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 - 13220 Enter Loan Application 2007-11-09 T 11:22:40 - 13219 Compute Installments 2007-11-09 T 11:22:45 - 13219 Notify Eligibility 2007-11-09 T 11:23:00 - 13219 Approve Simple Application 2007-11-09 T 11:24:30 - 13220 Compute Installements 2007-11-09 T 11:24:35 - … … … …
- 4. Automated Process Discovery • Relations-based – Alpha 4
- 5. Alpha Algorithm • Direct successors: A > B, B > C, C > D, A > C, C > B, B > E, E > F C > E, E > G B > D A B C D A C B E F • Causality: A → B, C → D, A → C, B → E, C → E, E → F, E → G , B → D • Concurrency: B ║ C • Exclusiveness: all other pairs A B C E G A C B D 5
- 6. Alpha Relations Matrix A B C D E F G A # → → → # # # B ← # || → → # # C ← || # → → # # D # ← ← # # # # E # ← ← # # → → F # # # # ← # # G # # # # ← # # 6
- 7. A B C D E F G A # → → # # # # B ← # || → → # # C ← || # → → # # D # ← ← # # # # E # ← ← # # → → F # # # # ← # # G # # # # ← # # Alpha Algorithm – Patterns 7 ⇔ a→ b, a→ c, b ║ c
- 8. Automated Process Discovery • Relations-based – Alpha: lossy (Badouel, Petri Nets 2012) – Alpha++ – Heuristics miner (frequency information) • Genetic • Region theory • Petri net synthesis • Integer Linear Programming (ILP) • … 8
- 11. Alignment-Based Conformance Log Model A B C D EA B B C Alignment E Fitness Precision How much behavior of the log is captured by the model? How accurate is the model describing the log? Munoz-Gama et al. Petri nets 2013 11
- 12. Deviance Mining 12 T1 <e11[d111:v111, …, d11n:v11n] e12[d121:v121, …, d12m:v12m] … e1p[d1p1:v1p1, …, d1pm:v1pm]> … Tq <eq1[dq11:vq11, …, dq1n:vq1n] eq2[dq21:vq21, …, dq2m:vq2m] … eqp[dqp1:vqp1, …, dqpm:vqpm]> T1 <e11[d111:v111, …, d11n:v11n] e12[d121:v121, …, d12m:v12m] … e1p[d1p1:v1p1, …, d1pm:v1pm]> … Tq <eq1[dq11:vq11, …, dq1n:vq1n] eq2[dq21:vq21, …, dq2m:vq2m] … eqp[dqp1:vqp1, …, dqpm:vqpm]> Find a function F: Trace Boolean (or probability [0…1]) s.t. •F is an accurate approximation of the given labeling •F is explainable, e.g. set of simple predicates
- 13. Simple “timely” claims Simple “slow” claims Deviance Mining via Model Delta Analysis 13 Suriadi et al. Understanding Process Behaviours in a Large Insurance Company in Australia. CAiSE 2013
- 14. Deviance Mining via Model Delta Analysis 14
- 15. Deviance Mining via Sequence Classification • Apply discriminative sequence mining methods to extract features characteristic of one class • Build classification models (e.g. decision trees) • Extract difference diagnostics from classification model C. Sun et al. Mining explicit rules for software process evaluation. 15
- 16. No Unified Foundation ≠ 16
- 17. (Prime) Event Structures • Model of concurrency based on events (occurrences of actions) and three relations – Causality – Conflict – Concurrency 17
- 18. Petri Nets Event Structures 18 b a b c d d c b d d a b c d d c b d d 0 1 2 3 4 5 6 7 8 0 4 5 6 7 a b c d d d 9
- 19. Nets With Cycles Prefix Unfolding 21 Petri net NPetri net N Complete prefix unfolding Complete prefix unfolding Causality-preserving prefix unfolding Causality-preserving prefix unfolding
- 20. Comparison of Event Structures 22 ? ES1 ES2 Armas-Cervantes et al. Behavioral Comparison of Process Models Based on […] Event Structures. BPM’2014 Partially Synchronized Product (PSP)
- 21. PSP Difference Statements 23
- 22. Comparison of Event Structures 24 In ES1, tasks C and B are mutually exclusive, while in ES2, B precedes C In ES1, tasks C and B are mutually exclusive, while in ES2, B precedes C ? ES1 ES2 Armas-Cervantes et al. Behavioral Comparison of Process Models Based on […] Event Structures. BPM’2014
- 23. BP-Diff: BPMN model comparison 25http://diffbp-bpdiff.rhcloud.com/
- 24. Event Logs Event Structures B || C Concurrency Oracle Run Merger 55 22 33 26
- 25. Event Structures for Log Delta Analysis 27 van Beest et al. Log delta analysis: Interpretable differencing of business process event logs. BPM’2015
- 26. Event Structures for Log Delta Analysis In L1, task C can be skipped after B, whereas in L2 it cannot In L1, task C can be skipped after B, whereas in L2 it cannot van Beest et al. Log delta analysis: Interpretable differencing of business process event logs. BPM’2015 28
- 27. Log Delta Analysis vs. Sequence Classification 448 cases 7329 events 363 cases, 7496 events Sequence classification 106- 130 statements IF |“NursingProgressNotes”| > 7.5 THEN L1 IF |“Nursing Progress Notes”| ≤ 7.5 AND |“Nursing Assessment”| > 1.5 THEN L2 … Sequence classification 106- 130 statements IF |“NursingProgressNotes”| > 7.5 THEN L1 IF |“Nursing Progress Notes”| ≤ 7.5 AND |“Nursing Assessment”| > 1.5 THEN L2 … Log delta analysis 48 statements In L1, “Nursing Primary Assessment” is repeated after “Medical Assign Start” and “Triage Request”, while in L2 it is not. … Log delta analysis 48 statements In L1, “Nursing Primary Assessment” is repeated after “Medical Assign Start” and “Triage Request”, while in L2 it is not. … 29 van Beest et al. Log delta analysis: Interpretable differencing of business process event logs. BPM’2015
- 28. Event Structures for Conformance Checking 30 ABDE ADBE ACDE ADCE
- 29. Event Structures for Conformance Checking 31 In the model, task C and B are in conflict, whereas in the log, B precedes C In the model, task C and B are in conflict, whereas in the log, B precedes C
- 30. … vs. alignment-based conformance checking 32 ABDE ADBE ACDE ADCE ABCDE ABDCE ADBCE A B C D E A C D E A B D C E A B D E A D B C E A D C E ?
- 31. Event Structures for Process Discovery? 33 ABDE ACDE ACDF Fold
- 33. The Road Ahead • Developing more accurate concurrency oracles – Dealing with (short) loops in parallel branches • Defining folding operators to generalize & simplify Petri nets synthesized from ES – Controlled generalization • Extensions to events with data payloads 35
Editor's Notes
- State-of-the-art conformance approach based on alignments Find the optimal trace in model that better describes each trace on the log. Occam RazorFitness Precision Other dimensions but here only this.