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Apromore: Advanced
Open-Source Process
Analytics on the Cloud
Raffaele Conforti
• School of Computing & Information System...
Process
discovery
Process
analysis
Process
redesign
Process
implementation
Process
monitoring
Process
identification
Proce...
Statistics-Based Techniques
Performance Dashboards
Model-Based Techniques
Process Mining
Database
Enterprise
System
Busine...
Process Mining
4
Open-source
• Apromore
• U. Melbourne,
U. Tartu, …
• bupaR
• U. Hasselt
• ProM
• TU/e
Lightweight
• Disco
• QPR Xpress
Ent...
Apromore – Advanced Process Analytics Platform
• Open-source BPM analytics platform
• Available as a Web-based application...
Performance Dashboards
Process Mining
Database
Enterprise
System
Business Process Analytics
Event log
Event stream
7
Process Mining
8
Enter Loan
Application
Retrieve
Applicant
Data
Compute
Installments
Approve
Simple
Application
Approve
Complex
Application...
Process Maps
A dependency graph of a log is a graph where:
• Each activity is represented by one node
• An arc from activi...
To cope with complex logs, process maps are used together with two operators:
1. Abstract the process map:
• Show only mos...
Alpha miner (α-miner)
• Simple, limited, not robust
Heuristics miner
• Robust to noise, fast, but can produce incorrect mo...
Automated Process Discovery Methods
Heuristics Miner
good F-score
complex models
semantic errors
13
Inductive Miner
high f...
Demo Time!
15
Process Mining
16
≠
Conformance Checking
Given a process model and an event log, find, describe,
and/or measure the differences between t...
Unfitting behaviour:
• Task C is optional (i.e. may be skipped) in the log
Additional behavior:
• The cycle including IGDF...
Interactive Model Repair
A. Armas Cervantes et al. “Interactive and Incremental Business Process Model Repair”, Proceeding...
19
Process
Model
Log
Unfitting behavior
(lack of fitness)
Additional behavior
(lack of precision)
Lack of
generalization
A...
Demo Time!
21
Process Mining
Given two logs, find the differences and root causes for variation
between the two logs
Variants Analysis
22
≠
• Model comparison
• Log delta analysis
Variants Analysis
23
Model
Comparison
L1 - Short stay
448 cases
7329 events
L2 - L...
• Detect process changes and pinpoint the time periods at which they occurred.
Drift Detection
Log:
ABCDF,
ABDCF,
ADBEDCF,...
Drift Detection
Demo Time!
27
Process Mining
Dotted charts
Timeline diagrams
Performance-enhanced
dependency graphs
Performance Mining
28
Demo Time!
Statistics-Based Techniques
Performance Dashboards
Model-Based Techniques
Process Mining
Database
Enterprise
System
Busine...
Process
Dashboards
Operational
dashboards
(runtime)
Tactical dashboards
(historical)
Strategic dashboards
(historical)
Pro...
Predictive Process Monitoring
Event
stream
Predictive
models
Detailed predictive dashboard
Alarm-based prescriptive dashbo...
Predictive Process Monitoring
• What is the next activity for this case?
• When is this next activity going to take place?...
Event log
Classifier /
Regressor
/
Outcome predictionAttributes
Traces
34
Event log
Structured
predictor
Next activity /
F...
Sequence encoding
35
Sequence encoding
▷ Index-based encoding
▷ Aggregation encoding
▷ LSTM (not yet in Apromore)
Predictive process monitoring workflow
Encoding Bucketing Learning
Training
set
Last state
Aggregation
Index-based
…
Zero
...
• Predict process outcome (e.g. “Is this loan offer going to be rejected?”)
• Predict process performance (e.g. “Will this...
Demo Time!
/
process mining
algorithms
live data
historical data
process model
differences,
root-causes
conformance
report
performa...
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Apromore: Advanced Business Process Analytics on the Cloud

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Tutorial delivered at the 16th International Conference on Business Process Management (BPM'2018), Sydney, Australia, 13 September 2018. The tutorial provides an introduction to process mining and predictive process monitoring using Apromore

Published in: Data & Analytics
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Apromore: Advanced Business Process Analytics on the Cloud

  1. 1. Apromore: Advanced Open-Source Process Analytics on the Cloud Raffaele Conforti • School of Computing & Information Systems The University of Melbourne, Australia Marlon Dumas • Institute of Computer Science University of Tartu, Estonia 1
  2. 2. Process discovery Process analysis Process redesign Process implementation Process monitoring Process identification Process architecture As-is process model Insights on issues and their impact To-be process model Executable process model/ change management plan Conformance and performance insights Business Process Analytics (Tools & Methods) Business Process Management  Monitoring
  3. 3. Statistics-Based Techniques Performance Dashboards Model-Based Techniques Process Mining Database Enterprise System Business Process Analytics Event log Event stream 3
  4. 4. Process Mining 4
  5. 5. Open-source • Apromore • U. Melbourne, U. Tartu, … • bupaR • U. Hasselt • ProM • TU/e Lightweight • Disco • QPR Xpress Enterprise-level • Celonis • Minit • myInvenio • ProcessGold • QPR • Signavio PI • … 5 Process Mining Tools: Overview 5
  6. 6. Apromore – Advanced Process Analytics Platform • Open-source BPM analytics platform • Available as a Web-based application, running on desktop or cloud • 50+ (really working) plugins 6
  7. 7. Performance Dashboards Process Mining Database Enterprise System Business Process Analytics Event log Event stream 7
  8. 8. Process Mining 8
  9. 9. Enter Loan Application Retrieve Applicant Data Compute Installments Approve Simple Application Approve Complex Application Notify Rejection Notify Eligibility Directly-follows graph (process map) Process model (e.g. BPMN) Automated process discovery Discover a model of the business process live data historical data Social network
  10. 10. Process Maps A dependency graph of a log is a graph where: • Each activity is represented by one node • An arc from activity A to activity B means that B is directly followed by A in at least one trace in the log Arcs in a dependency graph may be annotated with: • Absolute frequency: How many times B directly follows A? • Relative frequency: What percentage of times A is directly followed by B? • Time: What is the average time between the occurrence of A and the occurrence of B? 10
  11. 11. To cope with complex logs, process maps are used together with two operators: 1. Abstract the process map: • Show only most frequent activities • Show only most frequent arcs 2. Filter the traces in the event log • Remove all events that fulfil a condition • Remove traces that fulfil a condition Abstraction and Filtering 11
  12. 12. Alpha miner (α-miner) • Simple, limited, not robust Heuristics miner • Robust to noise, fast, but can produce incorrect models Inductive miner • Ensures that models are block-structured & correct Split miner • Produces deadlock-free but not necessarily structured models Discovering BPMN Process Models 12
  13. 13. Automated Process Discovery Methods Heuristics Miner good F-score complex models semantic errors 13 Inductive Miner high fitness no semantic errors simpler models low precision A. Augusto et al. Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs. In ICDM’2017. Split Miner high fitness no semantic errors simpler models Moderate precision
  14. 14. Demo Time!
  15. 15. 15 Process Mining
  16. 16. 16 ≠ Conformance Checking Given a process model and an event log, find, describe, and/or measure the differences between them
  17. 17. Unfitting behaviour: • Task C is optional (i.e. may be skipped) in the log Additional behavior: • The cycle including IGDF is not observed in the log Event log: ABCDEH ACBDEH ABCDFH ACBDFH ABDEH ABDFH Conformance Checking 17 García-Bañuelos et al. “Complete and Interpretable Conformance Checking of Business Processes” IEEE Transactions on Software Engineering 44(3): 262-290, 2018
  18. 18. Interactive Model Repair A. Armas Cervantes et al. “Interactive and Incremental Business Process Model Repair”, Proceedings of CoopIS’2017
  19. 19. 19 Process Model Log Unfitting behavior (lack of fitness) Additional behavior (lack of precision) Lack of generalization Accuracy of Automated Process Discovery
  20. 20. Demo Time!
  21. 21. 21 Process Mining
  22. 22. Given two logs, find the differences and root causes for variation between the two logs Variants Analysis 22 ≠
  23. 23. • Model comparison • Log delta analysis Variants Analysis 23 Model Comparison L1 - Short stay 448 cases 7329 events L2 - Long stay 363 cases 7496 events Log Delta Analysis In L1, “Nursing Primary Assessment” is repeated after “Medical Assign” and “Triage Request”, while in L2 it is not… N. van Beest et al. “Log Delta Analysis: Interpretable Differencing of Business Process Event Logs” Proc. of BPM’2015
  24. 24. • Detect process changes and pinpoint the time periods at which they occurred. Drift Detection Log: ABCDF, ABDCF, ADBEDCF, ABDECDF, ABCDEDEDF, ABDCF, AGBCDF, AGBDCF, AGDBEDCF, AGBDECDF, AGBCDEDED F, AGBDCF, Drift
  25. 25. Drift Detection
  26. 26. Demo Time!
  27. 27. 27 Process Mining
  28. 28. Dotted charts Timeline diagrams Performance-enhanced dependency graphs Performance Mining 28
  29. 29. Demo Time!
  30. 30. Statistics-Based Techniques Performance Dashboards Model-Based Techniques Process Mining Database Enterprise System Business Process Analytics Event log Event stream 30 30
  31. 31. Process Dashboards Operational dashboards (runtime) Tactical dashboards (historical) Strategic dashboards (historical) Process Performance Dashboards 31
  32. 32. Predictive Process Monitoring Event stream Predictive models Detailed predictive dashboard Alarm-based prescriptive dashboard Aggregate predictive dashboards Event logDatabase Enterprise System 32
  33. 33. Predictive Process Monitoring • What is the next activity for this case? • When is this next activity going to take place? • How long is this case still going to take until it is finished? • What is the outcome of this case? • Is the compensation going to be paid? Or rejected? 33
  34. 34. Event log Classifier / Regressor / Outcome predictionAttributes Traces 34 Event log Structured predictor Next activity / Future path prediction Attributes Traces Performance measure prediction Predictive Process Monitoring: General Approach
  35. 35. Sequence encoding 35
  36. 36. Sequence encoding ▷ Index-based encoding ▷ Aggregation encoding ▷ LSTM (not yet in Apromore)
  37. 37. Predictive process monitoring workflow Encoding Bucketing Learning Training set Last state Aggregation Index-based … Zero Cluster Prefix-length … Decision tree Random forest SVM … Buckets Models 39
  38. 38. • Predict process outcome (e.g. “Is this loan offer going to be rejected?”) • Predict process performance (e.g. “Will this claim take longer than 5 days to be handled?”) • Predict future events (e.g. “What activity is likely to be executed next? And after that?”) Event log Training module Training Validation Predictor Dashboard Runtime module Information system Predictions Stream (Kafka) Predictive model(s) Event stream Event stream Batched Predictions (CSV) Apromore Predictive process monitoring in Apromore 42
  39. 39. Demo Time!
  40. 40. / process mining algorithms live data historical data process model differences, root-causes conformance report performance measurements A ⇒ B Apromore in a nutshell 15 4,318 14 14 858 13 7,128 26 3,794 32 31 734 28 6,212 9 1,526 941 4,324 258 186 4,360 4,360 Created 4,360 Waiting for Support 12,587 Waiting for Customer 8,681 Resolved 5,023 Closed 4,360 Waiting for Internal 923 Escalation 42 Waiting for Approval 14 Waiting for Triage 31 Enterprise System predictions Apromore

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