Process Mining and Predictive Process Monitoring
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This document discusses process mining and predictive process monitoring. It begins with an overview of offline process mining techniques like process discovery, conformance checking, and deviance mining. It then discusses applying these techniques online for predictive process monitoring, including predicting outcomes, deviations, or failures. Various techniques are presented like nearest neighbor classification of partial traces and clustering traces before classification. The goal is to accurately predict outcomes during process execution based on control flow, data attributes, and textual case data.
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Process Mining and Predictive Process Monitoring
- 1. Process Mining and Predictive Process Monitoring Marlon Dumas marlon.dumas@ut.ee 1
- 2. Business Process Monitoring Dashboards & Reports Process MiningEvent stream DB 2
- 3. Offline Process Mining 3 / event log discovered model Discovery Conformance Deviance Difference diagnostics Performance input model Enhanced model event log’
- 4. Offline Process Mining: The Apromore Approach 4 / event log discovered model Discovery Conformance Deviance Difference diagnostics Performance input model Enhanced model event log’ BPMN Miner Log Delta Analysis Behavioral Alignment All integrated into: http://apromore.org
- 5. Automated Process Discovery 5 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 - … … … …
- 6. Automated Process Discovery: Before BPMN Miner (Heuristics Miner)
- 7. Automated Process Discovery: BPMN Miner
- 9. Conformance Checking with Trace Alignment A B C H E I J K C D I J K C E G A B C H E I J K C D I J K C E A B C H E I J K C E I K CJ F A B C H E I J K C D I J K G A B C H E I J K C D I J K G A B C H E I J K C E I KJ A B C H E I J K C E I KJ A B C D I J K C I J KE G A B C D I J K I J K C E G A B C H E I J K C I KJH H H H H H A B C H E I J K C I KJH A B C H I J K C E I KJH A B C H E I J K I K CJ FH A B C H E I J K I K CJ FH A B C D I J K C I J KEH A B C H E I J K I KJC D A B C H E I J K I KJC D A B C H E I J K I KJH A B C H E I J K I KJH A B C H E I J K GEC A B C H E I J K GEC A B C H E I J K EC A B C H E I J K EC A B C H I J K EC G A B C D I J K GEC A B C H I J K C F A B C H I J K C F A B C H I J K G A B C H E I J K A B C GE A IE J K A GE Activity occurs in the log only, but occurs in the model in another path Activity occurs in the model only and is not observed anywhere in the log Activity occurs in the model only, but occurs in the log in another trace Activity occurs both in the model and the log Legend
- 10. Difference statements Event log Input model PESM unfold PESL merge Partially Synchronized Product (PSP) compare extract differences Conformance Checking with Behavioral Alignment
- 11. Conformance Checking with Behavioral Alignment Desired conformance output: • task C is optional in the log • the cycle including IGDF is not observed in the log Log traces: ABCDEH ACBDEH ABCDFH ACBDFH ABDEH ABDFH L. Garcia-Banuelos, N.R. van Beest, M. Dumas, M. La Rosa, W. Mertens, Complete and Interpretable Conformance Checking of Business Processes, Technical Report, IEEE Transactions on Software Engineering, in press.
- 12. Given two logs, find the differences and root causes for variation or deviance between the two logs Simple claims and quick Simple claims and slow Deviance Mining MODEL S. Suriadi et al.: Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study. CAiSE 2013
- 13. 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. ICSSP’2013.
- 14. Difference statements Event log Input model PESM unfold PESL merge Partially Synchronized Product (PSP) compare extract differences Log Delta Analysis Difference statements Event log Input model PESM unfold PESL merge Partially Synchronized Product (PSP) compare extract differences 22 Difference statements Event log Input model PESM unfold PESL merge Partially Synchronized Product (PSP) compare extract differences N.R. van Beest, L. Garcia-Banuelos, M. Dumas, M. La Rosa, Log Delta Analysis: Interpretable Differencing of Business Process Event Logs. BPM 2015: 386-405
- 15. Sequence classification vs. log delta analysis L1 - Short stay 448 cases 7329 events L2 - Long stay 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 … Log delta analysis 48 statements In L1, “Nursing Primary Assessment” is repeated after “Medical Assign” and “Triage Request”, while in L2 it is not … N.R. van Beest, L. Garcia-Banuelos, M. Dumas, M. La Rosa, Log Delta Analysis: Interpretable Differencing of Business Process Event Logs. BPM 2015: 386-405
- 16. Apromore Process Analytics Platform (apromore.org) Open-source, highly scalable, SaaS BPM analytics platform M. La Rosa, H. Reijers, W. van der Aalst, R. Dijkman, J. Mendling, M. Dumas, L. Garcia-Banuelos “APROMORE: an advanced process model repository”, EXP.SYS.APP. 2011
- 17. How likely is it that a running process will become “deviant”? Will it end up in a negative outcome? Will it fail to meet its SLAs in the next 24 hours? Will it generate abnormal effort, costs or rework? Beyond Deviance Mining: Predictive Process Monitoring
- 18. Deviance Mining and Predictive Monitoring 19
- 19. 20 Debt repayment due Call the debtor Send a reminder Payment received Predictive Monitoring Example: Debt Recovery Process
- 20. Debt repayment due Call the debtor Send a reminder Send a warning Call the debtor Call the debtor Send to external debt collection agency Call the debtor Send a reminder Send a warning Call the debtor Call the debtorCall the debtor Call the debtor Call the debtor Call the debtor Call the debtor Call the debtor 21 Predictive Monitoring Example: Debt Recovery Process
- 21. Event log Classifier / Outcome Predictions Attributes Traces Predictive Process Monitoring: General Approach 22 Event log Regressor / structured predictor Future “paths” prediction Attributes Traces
- 22. Predictor Decision tree learning Decision tree Class estimation Current trace [Data+] Prediction Predictive Monitoring: Runtime Nearest-Neighbors Approach 23 Trace Processor kNN extraction (string-edit distance) Current trace [Event+] Event log Similar execution traces Feature extraction Labeled samples Current trace [Data+] F.M. Maggi, C. Di Francescomarino, M. Dumas, C. Ghidini. Predictive Monitoring of Business Processes. CAiSE'2014
- 23. • BPI Challenge 2011 dataset • Healthcare process at Dutch hospital • 1141 cases, avg length 14 events/case • Split normal-deviant via 5 predicates: φ1–φ5 • Prediction made at: • Start event (initial event) • Early event (ca. ¼ of the trace) • Middle Evaluation Setup 24
- 24. • Reasonably accurate at mid- point (AUC 0.78-0.88) • High runtime overhead 5-10 secs / prediction Evaluation Results 25
- 25. Predictive Process Monitoring: Cluster & Classify 26 Pre-processing Historical execution traces Running trace Runtime Clustering Clusters Control flow encoding Encoded control flow CONTROL FLOW Prefix extraction Trace Prefixes Predictive Monitoring Control flow encoding Data encoding Cluster(s) identification Classification Prediction Problem Prediction Supervised Learning Classifiers Data encoding Encoded data DATALabeling function AUC of 0.6 to 0.85 with a lot of variation
- 26. Each technique has its own hyperparameters Other parameters: • Trace prefix size • Voting mechanism • Interval choice in case of interval time predictions Predictive Process Monitoring: Cluster & Classify with Hyperparameter Optimization 27
- 27. • Four outcome labellings of a large real-life patient treatment dataset Experimental Settings Dataset preparation: •Training set (70%) •Validation set (20%) •Testing set (10%) Identification of the most suitable configurations (among 160) Evaluation of the identified configurations (with the testing set)
- 28. • No unique best configuration. • Accuracy is consistently high and accuracy on testing set consistent with the tuning. Evaluation Results Chiara Di Francescomarino, Marlon Dumas, Fabrizio Maria Maggi, Irene Teinemaa. Clustering-Based Predictive Process Monitoring. IEEE Transactions on Services Computing, 2017.
- 30. • Idea: One classifier per index • Classifier for prefixes of length 1 • Classifier for prefixes of length 2 • Etc. • Traces of length m encoded using an index-based schem • At runtime, classify a trace of length m using the corresponding classifier Index-Based Multi-Classifier 31 Anna Leontjeva, Raffaele Conforti, Chiara Di Francescomarino, Marlon Dumas, Fabrizio Maria Maggi: Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes. Proc. Of BPM 2015, pp. 297-313.
- 31. • Same as before, but feature vector of a prefix extended with Log-Likelihood Ratio of being in the deviant or regular class according to a Hidden-Markov Model Index-Based Multi-Classifier + HMM 32
- 34. Predictive Monitoring with Unstructured Data 35
- 35. Text mining 36
- 36. Text-Extended Index-Based Encoding 37 • Bag-of-N-grams • Weighted bag-of-N-grams • Latent Dirichlet Allocation (LDA) • Paragraph Vector (PV)
- 37. Debt Recovery Lead-to-contract # normal cases 13608 385 # deviant cases 417 390 Avg # words per doc 11 8 # lemmas 11822 2588 Evaluation Setup 38 • Data split: 80% train, 20% test (randomly) • Handling imbalance: oversampling • Classifiers: random forest and logistic regression • Evaluation metrics: F-Score and earliness • Parameter-tuning: grid search with 5-fold cross validation on training set
- 39. Ongoing work LSTM-Based Predictive Process Monitoring 40 Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas: Predictive Business Process Monitoring with LSTM Neural Networks. CoRR abs/1612.02130 (2016).
- 40. • Accurate, robust techniques to predict case outcome, covering control-flow, structured and textual data • LSTM-based architecture to predict • Next task + timestamp + resource or other attributes • Remaining execution path and time • All code available: • Clustering-based method: http://goo.gl/ykozBf • Index-based method: https://goo.gl/BQFk7k • Index-based method with textual features: https://goo.gl/a2DoWT • LSTM-based method: https://goo.gl/mkQDyy Online predictive process monitoring 41
Notas del editor
- As an example, we developed BPMN Miner, a technique that can discover hierarchical BPMN models which are structured in blocks (as structuredness generally makes models more understandable) and hierarchical (processes and subprocesses)
- Alternative approaches are based on replay, negative events etc.
- Each discrepancy falls under one of a set of disjoint patterns. For each pattern, we have a verbalization of the difference.
- The first statement characterizes the behavior observed in the log but not in the model: in the model, task C is compulsory, while in the log C is skippable The second statement characterizes the behavior observed in the model but not in the log Trace alignment would produce two optimal alignments: One between ABDEH of the log and ABCDHE of the model, the other between ABDFH of the log and ABCDFH of the model. From this one can infer that task C is optional in the log (move on log only). 1) However the number of misaligned traces is often very large, rendering this inference quite hard in practice. Visualizations, e.g. on top of Petri net, and at an aggregate level, can help, but fundamentally the problem is that trace alignment provides feedback at the level of individual traces, not at the level of behavioral relations observed in the log but not captured in the model. 2) Moreover, trace alignment would detect that there is escaping behavior starting with “Request addition information” at a trace prefix finishing with “Notify rejection”, but it will not identify that the extra behavior includes tasks IG and that IGDF is behavior that can be repeated in the model but not in the log. For example, task “Assess application” can be repeated in the model but not in the log.
- Each discrepancy falls under one of a set of disjoint patterns. For each pattern, we have a verbalization of the difference.
- Fair enough, our output is intuitively more interpretable, but let’s actually evaluate it.