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

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. 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. Statistics-Based Techniques
Performance Dashboards
Model-Based Techniques
Process Mining
Database
Enterprise
System
Business Process Analytics
Event log
Event stream
3

4. Process Mining
4

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. 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. Performance Dashboards
Process Mining
Database
Enterprise
System
Business Process Analytics
Event log
Event stream
7

8. Process Mining
8

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. 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. 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. 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. 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. Demo Time!

15. 15
Process Mining

16. 16
≠
Conformance Checking
Given a process model and an event log, find, describe,
and/or measure the differences between them

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. Interactive Model Repair
A. Armas Cervantes et al. “Interactive and Incremental Business Process Model Repair”, Proceedings of CoopIS’2017

19. 19
Process
Model
Log
Unfitting behavior
(lack of fitness)
Additional behavior
(lack of precision)
Lack of
generalization
Accuracy of Automated Process Discovery

20. Demo Time!

21. 21
Process Mining

22. Given two logs, find the differences and root causes for variation
between the two logs
Variants Analysis
22
≠

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. • 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. Drift Detection

26. Demo Time!

27. 27
Process Mining

28. Dotted charts
Timeline diagrams
Performance-enhanced
dependency graphs
Performance Mining
28

29. Demo Time!

30. Statistics-Based Techniques
Performance Dashboards
Model-Based Techniques
Process Mining
Database
Enterprise
System
Business Process Analytics
Event log
Event stream
30
30

31. Process
Dashboards
Operational
dashboards
(runtime)
Tactical dashboards
(historical)
Strategic dashboards
(historical)
Process Performance Dashboards
31

32. Predictive Process Monitoring
Event
stream
Predictive
models
Detailed predictive dashboard
Alarm-based prescriptive dashboard
Aggregate predictive dashboards
Event
logDatabase
Enterprise
System
32

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. 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. Sequence encoding
35

36. Sequence encoding
▷ Index-based encoding
▷ Aggregation encoding
▷ LSTM (not yet in Apromore)

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. • 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. Demo Time!

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