Data-driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

1. Data-driven Process Discovery Revealing Conditional 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 PAGE 2 / 20Source: van der Aalst, W.M.P.: Process Mining - Data Science in Action, Second Edition. Springer (2016)

4. “Happy Path” Discovery PAGE 3 / 20

5. Noise vs. potentially interesting 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 the Art – 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-aware Heuristic 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 Heuristic Miner (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) Dependency Conditions 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) Conditional Directly-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 – Can we 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 – Does it work in practice? PAGE 18 / 20 Hospital Billing – Event Log (100,000 cases)

20. Conclusion & Future Work 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

Data-driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

Data-driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

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Data-driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs

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