Unbiased, Fine-Grained Description of Processes Performance from Event Data
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International Conference on Business Process Management 2018 (BPM2018 Sydney). Performance is central to processes management and event data pro-vides the most objective source for analyzing and improving performance. Currentprocess mining techniques give only limited insights into performance by aggre-gating all event data for each process step. In this paper, we investigate processperformance of all process behaviors without prior aggregation. We propose theperformance spectrumas a simple model that maps all observed flows betweentwo process steps together regarding their performance over time. Visualizing theperformance spectrum of event logs reveals a large variety of very distinctpatternsof process performanceand performance variability that have not been describedbefore. We provide a taxonomy for these patterns and a comprehensive overviewof elementary and composite performance patterns observed on several real-lifeevent logs from business processes and logistics. We report on a case study whereperformance patterns were central to identify systemic, but not globally visibleprocess problems.
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Unbiased, Fine-Grained Description of Processes Performance from Event Data
- 1. Unbiased, Fine-Grained Description of Processes Performance from Event Data Vadim Denisov, Dirk Fahland, Wil van der Aalst
- 2. Material Handling System (MHS) Example 1 Check-In Baggage Handling System (BHS)
- 3. Example of BHS Performance Problem 2 Check-In Transfer Scanner 1 Scanner 2 Merge Sorter Exit Dead-lock on the Sorter Loop
- 4. Example of a Real BHS Model 3 Transfer Scanner 2 Sorter Exit
- 5. Questions about the model: • Do all cases take 12m from Scanner 2 to Merge? • When and why did bottlenecks happen? • How do process variants divert/merge? BHS: Questions about Performance 4 Check-In Transfer Merge Sorter Scanner 1 Scanner 2 Exit 1m 1m 10m 12m 6m 3m Baggage Handling System
- 6. Questions about the model: • Do all cases take 12m from Scanner 2 to Merge? • When and why did bottlenecks happen? • How do process variants divert/merge? BHS: Questions about Performance 5 Check-In Transfer Merge Sorter Scanner 1 Scanner 2 Exit 1m 1m 10m 12m 6m 3m Baggage Handling System Generalized questions: • What is the actual distribution of durations? • What are timelines of individual cases? • What is performance over time? • How do cases and process steps influence each other?
- 7. Business Process: Questions about Performance 6 Road Traffic Fines Management process Generalized questions: • What is the actual distribution of durations? • What are timelines of individual cases? • What is performance over time? • How do cases and process steps influence each other?
- 8. 1. Visual Analytics: a different way to visualize event data 2. New findings in well-known data 3. Introduction to performance patterns 4. Evaluation and a new tool How to Represent System Performance in Dynamic? 7 Event Log Annotated Model ? Performance Abstraction Levels
- 9. Performance in Dynamic 8 A B Sub-trace: A(time=t1) → B(time=t2) Cases started/stopped/pending/… normal speed 2 times slower 3 times slower very slow Time A B t1 t2 StepStep Performance Spectrum
- 10. 1. Visual Analytics: a different way to visualize event data 2. New findings in well-known data (Road Traffic Fines Management process) 3. Introduction to performance patterns 4. Evaluation and a new tool How to Represent System Performance in Dynamic? 9 Performance Spectrum Event Log Annotated ModelPerformance Abstraction Levels
- 11. Example: Unordered Execution vs. FIFO 10 Create Fine Payment Insert Fine Notification Add Penalty
- 12. Example: Batching 11 Create Fine Send Fine Insert Fine Notification
- 13. Example: Complete Case Variant 12 Send for Credit Collection 5d Create Fine Send Fine Insert Fine Notification Add Penalty
- 14. 1. Visual Analytics: a different way to visualize event data 2. New findings in well-known data 3. Introduction to performance patterns 4. Evaluation and a new tool How to Represent System Performance in Dynamic? 13 Performance Spectrum ? Event Log Annotated ModelPerformance Abstraction Levels
- 15. Performance Patterns Taxonomy: Order of Cases 14 unordered FIFO batching on start batching on end
- 16. Full Taxonomy → See in Paper 15 ORDER unordered FIFO LIFO batching on start batching on end constant speed batching on start and end CLASSES PRESENTED 1 >1 WORKLOAD TRENDS (TOTAL OR FOR A CLASS) steady variable growing falling AMOUNT OF WORKLOAD zero non-zero low medium high SIZE segment one subsequence ab bc cd several subsequences kl lm mn ab bc R1 R2 OCCURRENCE globally as a local instance REPETITIONS single periodicsystematic arbitrary TYPE detailed composite aggregated VARIANTS CONTAINED 1 >1 Aggregation functions: {cases started, cases stopped, diff. of start/stop, cases pending} <exact number> exact number spiky scheduled variable speed WORKLOAD CHARACTER continuous sparse PERFORMANCE IN CONTEXT slower faster the same WORKLOAD PERFORMANCE SCOPE Classifiers: quartile-based, median-proportional (for MHS) DURATION= <abs. value> SHAPE
- 17. 1. Visual Analytics: a different way to visualize event data 2. New findings in well-known data 3. Introduction to performance patterns 4. New tool and evaluation How to Represent System Performance in Dynamic? 16 Event Log Annotated ModelPerformance Abstraction Levels Performance Spectrum Performance Patterns
- 18. Performance Spectrum Miner and Evaluation 17 https://github.com/processmining-in-logistics/psm See it in action: Demo Session 2! ✓ ProM plugin & Stand-alone ✓ Documentation ✓ Open-Source LGPL-3 ✓ Java-compatible • Real-world problem (Vanderlande, BHS of a major European airport) • BPI Challenge 2018: applying to business processes
- 19. Business Process vs. Material Handling System 18 Road Traffic Fine Management Process Baggage Handling System
- 20. Spectra of Public Event Logs from 4TU Datacenter 19 BPI’12 BPI’14 BPI’15-1 BPI’15-2 BPI’15-5 BPI’17 Hospital Billing RTFM
- 21. New visual analytics technique: 1. performance of the whole process over time (all cases) 2. explore how process steps/cases influence each other 3. analyze processes on a higher abstraction level: performance patterns Conclusions 20 Performance Spectrum Performance Patterns Event Log Annotated ModelPerformance Abstraction Levels
- 22. Future Work 21 • automatic ordering of segments • dealing with parallel activities • automatic detection of patterns and annotating process models with them • predicting performance spectra to detect performance issues of MHS