![Step (4) – Discover a Causal Net with Conditional Dep. #2
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Causal Net
Step 4.2: Expand with Conditional Dependencies
• Dependency Threshold (θdep)
• Condition Threshold (θcon) [e.g., AUROC, Kappa, F-Score, ..]
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a ⇒C,L b
Conditional
dependency measure](https://image.slidesharecdn.com/caise-data-driven-discovery-170616070325/75/Data-driven-Process-Discovery-Revealing-Conditional-Infrequent-Behavior-from-Event-Logs-15-2048.jpg)
Uploaded byFelix Mannhardt
Data-driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs
The document presents a data-aware heuristic miner (DHM) for process discovery that aims to identify conditional infrequent behavior within event logs, addressing limitations of traditional noise filtering techniques. It details the method's steps, including building dependency conditions and discovering causal nets, emphasizing the influence of data attributes on control-flow discovery. The approach has been validated on large real-life event logs and aims to extend to more complex behavioral patterns.
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Data-driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs
- 1. Data-driven Process Discovery RevealingConditional Infrequent Behavior from Event Logs Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers, Wil M.P. van der Aalst
- 2. Process Discovery PAGE 1/ 20 Three traces recorded for three process instances
- 3. The Noise Challenge PAGE2 / 20Source: van der Aalst, W.M.P.: Process Mining - Data Science in Action, Second Edition. Springer (2016)
- 4.
- 5. Noise vs. potentiallyinteresting infrequent behavior PAGE 4 / 20 Infrequent behavior What exactly is noise in event logs? • Infrequent out-of-order events • Recording errors • Exceptional behavior Random / No explanation
- 6. State of theArt – Based on Control-flow & Frequencies PAGE 5 / 20 Inductive miner Heuristics miner Existing noise filtering techniques are based on control-flow perspective!
- 7. Proposed Method: Data-awareHeuristic Miner PAGE 6 / 20 Ⓐ priority = white Ⓑ nurse = Alice Ⓒ type = out Ⓐ Ⓑ Ⓒ Source: van der Aalst, W.M.P.: Process Mining - Data Science in Action, Second Edition. Springer (2016)
- 8. Overview: Data-aware HeuristicMiner (DHM) PAGE 7 / 20 𝑎 > 𝐶 𝑎,𝑏,𝐿 𝑏 Conditional directly-follows relation a ⇒C,L b Conditional dependency measure Dependency conditions (C) C Event log (L) Causal Net (1) (4) (3) (2)(2)
- 9. Step (1) DependencyConditions PAGE 8 / 20 Binary classifiers that predict occurrence of directly-follows relations: YES: Activity b directly-follows activity a NO: Other activities (≠b) directly-follow a Any classifiers can be employed! (we used C4.5) Dependency conditions (C) C Event log (L) (1)
- 10. Building Dependency Conditions– Example #1 PAGE 9 / 20 Relation: Register, END (Ⓐ) Count Relation Class Priority Nurse … 1430 (Register, END) 1 white … … 39780 (Register, X-Ray) 0 green … … 49295 (Register, Check) 0 orange … … 9491 (Register, Visit) 0 red … … Classifier: if priority = white then YES, otherwise NO
- 11. Building Dependency Conditions– Example #2 PAGE 10 / 20 Relation: X-Ray, Visit (Ⓑ) Count Relation Class Priority Nurse … 20400 (X-Ray, Visit) 1 … Alice … 7923 (X-Ray, Final Visit) 0 … Peter … (X-Ray, Check) Detected as parallel activities Classifier: if nurse = Alice then YES, otherwise NO
- 12. Step (2) ConditionalDirectly-follows Relation PAGE 11 / 20 L Dependency conditions C Event Log 𝑎 > 𝐶 𝑎,𝑏,𝐿 𝑏 Conditional directly-follows relation Relation: a followed by b under condition Ca,b 𝑋 > 𝐶 𝑋,𝑉,𝐿 𝑉 = 1 𝑉 > 𝐶 𝑋,𝑉,𝐿 𝑋 = 0
- 13. Step (3) –Conditional Dependency Measure PAGE 12 / 20 𝑎 > 𝐶 𝑎,𝑏,𝐿 𝑏 Conditional directly-follows relation 𝑎 ⇒ 𝐶,𝐿 𝑏 Conditional dependency measure Adapting the Heuristics Miner for conditional directly-follows 𝑎 ⇒ 𝐶,𝐿 𝑏 = 𝑎 > 𝐶 𝑎,𝑏,𝐿 𝑏 − 𝑏 > 𝐶 𝑎,𝑏,𝐿 𝑎 𝑎 > 𝐶 𝑎,𝑏,𝐿 𝑏 + 𝑏 > 𝐶 𝑎,𝑏,𝐿 𝑎 + 1 , for 𝑎 ≠ 𝑏 𝑎 > 𝐶 𝑎,𝑎,𝐿 𝑎 𝑎 > 𝐶 𝑎,𝑎,𝐿 𝑎 + 1 , otherwise
- 14. Step (4) –Discover a Causal Net with Conditional Dep. PAGE 13 / 20 a ⇒C,L b Conditional dependency measure Causal Net Step 4.1: Build Unconditional Dependency Graph • Observation Threshold (θobs) • Dependency Threshold (θdep)
- 15. Step (4) –Discover a Causal Net with Conditional Dep. #2 PAGE 14 / 20 Causal Net Step 4.2: Expand with Conditional Dependencies • Dependency Threshold (θdep) • Condition Threshold (θcon) [e.g., AUROC, Kappa, F-Score, ..] Ⓐ Ⓑ Ⓒ a ⇒C,L b Conditional dependency measure
- 16. Step (4) –Discover a Causal Net with Conditional Dep. #3 PAGE 15 / 20 Causal Net Step 4.3: Connect Tasks added a ⇒C,L b Conditional dependency measure
- 17. Step (4) –Discover a Causal Net with Conditional Dep. #4 PAGE 16 / 20 Causal Net Step 4.4: Build Causal Net as in the Heuristic Miner • Binding Threshold (θbin) a ⇒C,L b Conditional dependency measure
- 18. Evaluation – Canwe rediscover conditions? (Synthetic) PAGE 17 / 20 • Noise level 0.05 means that in 5% of the traces 1 event is out-of-order • Compared three methods • Heuristic Miner without filtering (HMA) • Heuristic Miner with filtering (HMF) • Data-aware Heuristic Miner (DHM) • GED-based comparison since we want to evaluate at dependency level
- 19. Evaluation – Doesit work in practice? PAGE 18 / 20 Hospital Billing – Event Log (100,000 cases)
- 20. Conclusion & FutureWork PAGE 19 / 20 Implemented in ProM 6.7: Data-aware Heuristic Miner • Data-first approach: • Data attributes influence control- flow discovery • Conditional infrequent behavior • Combines classification methods and heuristic process discovery • Validated on large real-life event logs • Extend the idea to more complex patterns of behavior • Long-term dependencies • Duplicate activities • Suggest suitable parameter settings / hyperparameter optimization
- 21. Questions @fmannhardt - f.mannhardt@tue.nl- fmannhardt.de