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Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs
The document discusses the Split Miner, a process discovery tool that extracts accurate and simple business process models from event logs. It evaluates the quality of discovered models based on fitness, precision, generalization, complexity, and execution time, showcasing its effectiveness against other methodologies. Future work aims to enhance filtering, reduce the complexity of gateways, and develop real-time process discovery capabilities.
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Presentation introduction on Split Miner, a tool for discovering business process models, by notable researchers.
Explains fundamental concepts of business processes, including events such as start and end events, gateways, and splits.
Detailed sample of event logs and traces, showing case IDs, activities, and timestamps associated with a loan application process.
Criteria for evaluating process models, emphasizing Recall (Fitness), Precision, F-Score, Generalization, and Complexity metrics.
Examines different process discovery methods like Heuristic and Inductive Miner, highlighting their strengths and weaknesses.
Defines goals for the development of the Split Miner, focusing on balanced fitness and precision, high F-Score, low complexity, and execution time.
Describes the five steps involved in converting event logs into process models, detailing various discovery techniques.
Further elaboration on tracing behaviors within event logs, showcasing observed patterns during model generation.
Continues from previous slide, further detailing traces and their significance in process model generation.
Focuses on the formation and utility of directly-follows graphs in process modeling.
Further explanation on how directly-follows graphs contribute to process modeling.
A brief intermission in the presentation.
Advanced features of directly-follows graphs impacting process modeling techniques.
Continues exploring advanced topics regarding loops and graph structures in process discovery.
Concludes the explanation of process model generation.
Explains the setup for evaluation using real-life event logs across various domains and metrics.
Presents results showing Split Miner's dominance over other methods based on hyperparameter optimization.
Highlights successful outcomes of Split Miner with default parameters, focusing on F-score and execution time advantages.
Introduces the integration of Split Miner within the Apromore framework.
Outlines potential improvements and future development areas for optimizing Split Miner.
Wraps up the presentation and opens the floor for questions.
Contains supplementary slides with extra information.
Describes the principles of concurrency discovery to identify parallel processes in a model.
Further elaboration on concurrency discovery principles with examples.
Explains methods for filtering event logs to retain frequent behaviors, enhancing model quality.
Details the process of discovering splits in event logs and their representation in process models.
Discusses complex scenarios in split discovery and representation in process models.
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Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs
- 1. Split Miner: Discovering Accurateand Simple Business Process Models from Event Logs Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa 117th IEEE International Conference on Data MiningSplit Miner
- 2. What is aBusiness Process? 217th IEEE International Conference on Data MiningSplit Miner Start event End event Parallel (AND) Gateway Exclusive (XOR) Gateway Split (AND) Gateway Join (XOR) Gateway
- 3. Event Logs &Traces Case ID Activity Timestamp … 01 Enter Loan Application 2007-11-09 T 11:20:10 - 01 Compute Installments 2007-11-09 T 11:25:15 - 02 Enter Loan Application 2007-11-09 T 11:30:40 - 01 Retrieve Applicant Data 2007-11-09 T 11:40:50 - 02 Retrieve Applicant Data 2007-11-09 T 11:50:00 - 02 Compute Installments 2007-11-09 T 12:00:30 - 02 Notify Eligibility 2007-11-09 T 12:10:45 - 03 Enter Loan Application 2007-11-09 T 13:23:15 - 03 Compute Installments 2007-11-09 T 13:30:35 - 01 Notify Rejection 2007-11-09 T 14:45:00 - 02 Approve Simple Application 2007-11-09 T 15:00:30 - … … … … 317th IEEE International Conference on Data MiningSplit Miner
- 4. Quality of DiscoveredProcess Models — Recall (a.k.a. Fitness): behaviour recorded in the event log reproducible by the model (%) — Precision: behaviour producible by the model observed in the event log (%) — F-Score: (2 * fitness * precision) / (fitness + precision) — Generalization: behaviour producible by the model eventually recorded in the event log (%) — Complexity: a set of metrics estimating simplicity and understandability of the process model 417th IEEE International Conference on Data MiningSplit Miner
- 5. Process Discovery Methods HeuristicsMiner good F-score complex semantic errors tuning complexity 517th IEEE International Conference on Data MiningSplit Miner Inductive Miner high fitness no semantic errors simple low precision
- 6. Our Objectives Balanced Fitnessand Precision High F-Score High Generalization Low Complexity Low Execution Time Tuning Simplicity 617th IEEE International Conference on Data MiningSplit Miner
- 7. From Event Logto Process Model in 5 Steps Directly-Follows Graph and Loops Discovery Filtering Concurrency Discovery Splits Discovery Joins Discovery 717th IEEE International Conference on Data MiningSplit Miner Event Log Process Model
- 8. Trace #obs a »b » c » g » e » h 10 a » b » c » f » g » h 10 a » b » d » g » e » h 10 a » b » d » e » g » h 10 a » b » e » c » g » h 10 a » b » e » d » g » h 10 a » c » b » e » g » h 10 a » c » b » f » g » h 10 a » d » b » e » g » h 10 a » d » b » f » g » h 10 817th IEEE International Conference on Data MiningSplit Miner Directly-Follows Graph and Loops Discovery Concurrency DiscoveryEvent Log Filtering Splits Discovery Joins Discovery Process Model
- 9. Trace #obs a »b » c » g » e » h 10 a » b » c » f » g » h 10 a » b » d » g » e » h 10 a » b » d » e » g » h 10 a » b » e » c » g » h 10 a » b » e » d » g » h 10 a » c » b » e » g » h 10 a » c » b » f » g » h 10 a » d » b » e » g » h 10 a » d » b » f » g » h 10 917th IEEE International Conference on Data MiningSplit Miner Directly-Follows Graph and Loops Discovery Concurrency DiscoveryEvent Log Filtering Splits Discovery Joins Discovery Process Model
- 10. 1017th IEEE InternationalConference on Data MiningSplit Miner Directly-Follows Graph and Loops Discovery Concurrency DiscoveryEvent Log Filtering Splits Discovery Joins Discovery Process Model (b || c) (b || d) (d || e) (e || g)
- 11. 1117th IEEE InternationalConference on Data MiningSplit Miner Directly-Follows Graph and Loops Discovery Concurrency DiscoveryEvent Log Filtering Splits Discovery Joins Discovery Process Model
- 12. 1217th IEEE InternationalConference on Data MiningSplit Miner Break for Make-up!
- 13. 1317th IEEE InternationalConference on Data MiningSplit Miner Directly-Follows Graph and Loops Discovery Concurrency DiscoveryEvent Log Filtering Splits Discovery Joins Discovery Process Model (b || c) (b || d) (d || e) (e || g)
- 14. 1417th IEEE InternationalConference on Data MiningSplit Miner Directly-Follows Graph and Loops Discovery Concurrency DiscoveryEvent Log Filtering Splits Discovery Joins Discovery Process Model
- 15. 1517th IEEE InternationalConference on Data MiningSplit Miner Done!
- 16. Evaluation Setup — 12publicly available real-life event logs domains: finance, health care, law enforcement, government, information technology recorded events: 6,660 to 561,470 recorded traces: 681 to 150,370 — 5 state-of-the-art process model discovery methods — hyperparameter-optimization evaluation — default-parameters evaluation 1617th IEEE International Conference on Data MiningSplit Miner
- 17. Hyperparameter-optimization Results 10 timesout of 12 Split Miner (green points) pareto-dominates the other methods 1717th IEEE International Conference on Data MiningSplit Miner
- 18. Default-parameters Results — 11times Split Miner achieved the highest F-score balancing fitness and precision — 9 times Split Miner achieved an F-score greater than 0.80 high F-score — 10 times Split Miner scored the highest generalization high generalization — Split Miner always discovered at least the second simplest process model low complexity — Split Miner was always 2 to 6 times faster than the second fastest low execution time — Split Miner has only 2 simple and well documented tuning parameters tuning simplicity 1817th IEEE International Conference on Data MiningSplit Miner
- 19. Split Miner inApromore 1917th IEEE International Conference on Data MiningSplit Miner
- 20. Future Work — Improvethe Filtering to achieve higher F-score — Minimize the use of the OR-gateways — Turn Split Miner into an Run-Time process discovery method — Apply a-posteriori algorithms to improve model simplicity 2017th IEEE International Conference on Data MiningSplit Miner
- 21. Thanks for Listening! AnyQuestions? 2117th IEEE International Conference on Data MiningSplit Miner
- 22. Extra Slides 2217th IEEEInternational Conference on Data MiningSplit Miner
- 23. Concurrency Discovery b ||c 1. There is an edge from b to c 2. There is an edge from c to b 3. b and c are not a short-loop 4. edges (b, c) and (c, b) have similar frequencies ||a →b| − |b →a|| |a →b| + |b →a| < ε 2317th IEEE International Conference on Data MiningSplit Miner
- 24. Concurrency Discovery 2417th IEEEInternational Conference on Data MiningSplit Miner ε = 0.2 b || c b || d d || e e || g
- 25. Filtering — Retain themost frequent behaviour (high fitness) — Retain the minimum number of edges (high precision, low complexity) — Guarantee each node on a path from start to end (high fitness) 1. Create a set B 2. Add to B the most frequent incoming and outgoing edges of each node of the DFG 3. Evaluate the percentile-ƞ on the frequencies of the edges in B 4. Add to B the edges with greater frequency than the percentile-ƞ 5. Remove all the edges of the DFG 6. Starting from the most frequent edge e in B, add e to the DFG if: the source of e does not have outgoing edges OR the target of e does not have incoming edges. 2517th IEEE International Conference on Data MiningSplit Miner
- 26. Split Discovery b ||c b || d b || c, d c || b d || b 2617th IEEE International Conference on Data MiningSplit Miner Split A successors: b, c, d
- 27. Split Discovery split: A b|| c, d c || b d || b Xor(c, d) b || c, d c, d || b And(b, Xor(c,d)) b, c, d || — 2717th IEEE International Conference on Data MiningSplit Miner