Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.

1. Chapter 1
Introduction
prof.dr.ir. Wil van der Aalst
www.processmining.org

2. Overview
Chapter 1
Introduction
Part I: Preliminaries
Chapter 2 Chapter 3
Process Modeling and Data Mining
Analysis
Part II: From Event Logs to Process Models
Chapter 4 Chapter 5 Chapter 6
Getting the Data Process Discovery: An Advanced Process
Introduction Discovery Techniques
Part III: Beyond Process Discovery
Chapter 7 Chapter 8 Chapter 9
Conformance Mining Additional Operational Support
Checking Perspectives
Part IV: Putting Process Mining to Work
Chapter 10 Chapter 11 Chapter 12
Tool Support Analyzing “Lasagna Analyzing “Spaghetti
Processes” Processes”
Part V: Reflection
Chapter 13 Chapter 14
Cartography and Epilogue
Navigation
PAGE 1

3. Data explosion
PAGE 2

4. The World's Technological Capacity to Store, Communicate, and Compute
Information by Martin Hilbert and Priscila López (DOI 10.1126/science.1200970)
PAGE 3

5. PAGE 4

6. Example process model
examine
thoroughly
c1 c3 pay
compensation
examine
start register casually decide c5 end
request
c2 c4 reject
check ticket request
reinitiate
request
PAGE 5

7. Same process in terms of BPMN
rather than Petri nets
examine
thoroughly
pay
examine
compensation
casually
register
decide
request
start reject end
check ticket
request
reinitiate
request
PAGE 6

8. What are process models used for?
• insight: while making a model, the modeler is triggered to view the
process from various angles;
• discussion: the stakeholders use models to structure discussions;
• documentation: processes are documented for instructing people or
certification purposes (cf. ISO 9000 quality management);
• verification: process models are analyzed to find errors in systems or
procedures (e.g., potential deadlocks);
• performance analysis: techniques like simulation can be used to
understand the factors influencing response times, service levels, etc.;
• animation: models enable end users to “play out” different scenarios
and thus provide feedback to the designer;
• specification: models can be used to describe a PAIS before it is
implemented and can hence serve as a “contract” between the
developer and the end user/management; and
• configuration: models can be used to configure a system.
PAGE 7

9. Limitations
• Executable models may be used to force people to
work in a particular manner.
• However, most models are not well-aligned with
reality.
• Most hand-made models are disconnected from
reality and provide only an idealized view on the
processes at hand: “paper tigers”.
• Given (a) the interest in process models, (b) the
abundance of event data, and (c) the limited quality
of hand-made models, it seems worthwhile to relate
event data to process models: process mining!
PAGE 8

10. BPM life-cycle showing the classical
uses of process models
diagnosis/
requirements
adjustment insight
discussion performance
animation analysis
enactment/
(re)design
monitoring data models
verification
documentation
specification
configuration/
implementation
configuration
PAGE 9

11. The three main types of process mining:
discovery, conformance, and enhancement
supports/
“world” business
controls
processes software
people machines system
components
organizations records
events, e.g.,
messages,
specifies transactions,
models
configures etc.
analyzes
implements
analyzes
discovery
(process) event
conformance
model logs
enhancement
PAGE 10

12. Orthogonal: Perspectives
• The control-flow perspective focuses on the control-
flow, i.e., the ordering of activities.
• The organizational perspective focuses on
information about resources hidden in the log, i.e.,
which actors (e.g., people, systems, roles, and
departments) are involved and how are they related.
• The case perspective focuses on properties of
cases, e.g., cases can also be characterized by the
values of the corresponding data elements.
• The time perspective is concerned with the timing
and frequency of events.
PAGE 11

13. Starting point: event log
XES, MXML, SA-MXML, CSV, etc.
PAGE 12

14. Simplified event log
a = register request,
b = examine thoroughly,
c = examine casually,
d = check ticket,
e = decide,
f = reinitiate request,
g = pay compensation,
and h = reject request
PAGE 13

15. Process
discovery
b
examine
thoroughly
g
c1 c3 pay
c compensation
a examine
e
start register casually decide c5 end
request
h
c2 d c4 reject
check ticket request
f
reinitiate
request
PAGE 14

16. Another example
b
c1 examine c3
thoroughly
a e h
start register decide c5 reject end
request request
d
c2 check ticket c4
PAGE 15

17. Beyond discovery:
conformance and enhancement
supports/
“world” business
controls
processes software
people machines system
components
organizations records
events, e.g.,
messages,
specifies transactions,
models
configures etc.
analyzes
implements
analyzes
discovery
(process) event
conformance
model logs
enhancement
PAGE 16

18. Another event log
b
examine
thoroughly
g
c1 c3 pay
c compensation
a examine
e
start register casually decide c5 end
request
h
c2 d c4 reject
check ticket request
f
reinitiate
request PAGE 17

19. Extension
The event log can be used to
discover roles in the organization
(e.g., groups of people with similar
work patterns). These roles can be Performance information (e.g., the
used to relate individuals and average time between two
activities. subsequent activities) can be
extracted from the event log and
visualized on top of the model.
Role A: Role E: Role M:
Assistant Expert Manager
Decision rules (e.g., a decision tree
based on data known at the time a
Pete Sue Sara particular choice was made) can be
learned from the event log and used
Mike Sean to annotated decisions.
Ellen E
b
A
examine
thoroughly
A
g
A M
c1 c3 pay
c compensation
a examine
e
A
start register casually
A decide c5 end
request
h
c2 d c4 M reject
check ticket request
f
reinitiate
request PAGE 18

20. Play-Out
process model event log
PAGE 19

21. Play-In
event log process model
PAGE 20

22. Replay
• extended model
showing times,
frequencies, etc.
• diagnostics
• predictions
• recommendations
event log process model
PAGE 21

23. Replay
• Connecting models to real events is crucial!
• Possible uses:
− Conformance checking
− Repairing models
− Extending the model with frequencies and temporal
information
− Constructing predictive models
− Operational support (prediction, recommendation,
etc.)
PAGE 22

24. Desire lines in process models
PAGE 23

25. Trends and terms
• Business Process Management (BPM)
• Business Intelligence (BI)
• Online Analytical Processing (OLAP)
• Business Activity Monitoring (BAM)
• Complex Event Processing (CEP)
• Corporate Performance Management (CPM)
• Visual Analytics (VA)
• Predictive Analytics (PA)
• Continuous Process Improvement (CPI)
• Total Quality Management (TQM)
• Six Sigma
PAGE 24

26. Six Sigma
• Six Sigma was originally developed by Motorola in
the early 1980s.
• DMAIC approach:
− Define the problem and set targets,
− Measure key performance indicators and collect data,
− Analyze the data to investigate and verify cause-and-
effect relationships,
− Improve the current process based on this analysis,
− Control the process to minimize deviations from the
target.
PAGE 25

27. [μ-6σ, μ+6σ] with a 1.5σ shift
A process that “runs at
Six Sigma” has only
3.4 defective cases per
million cases, i.e., on
average 99.9997% of
the cases is handled
properly.
PAGE 26

28. Performance improvement versus
compliance
• Organizations are also putting more emphasis on
corporate governance, risk, and compliance.
• Scandals (Enron, Tyco, Adelphia, Peregrine,
WorldCom, etc.) have fueled interest in more
rigorous auditing practices.
• New legislation such as the Sarbanes-Oxley Act
(SOX) of 2002 and the Basel II Accord of 2004
emerged as a result.
• Importance of verifying whether organizations
operate “within their boundaries” is increasing.
PAGE 27

29. Outlook
Chapter 1
Introduction
Part I: Preliminaries
Chapter 2 Chapter 3
Process Modeling and Data Mining
Analysis
Part II: From Event Logs to Process Models
Chapter 4 Chapter 5 Chapter 6
Getting the Data Process Discovery: An Advanced Process
Introduction Discovery Techniques
Part III: Beyond Process Discovery
Chapter 7 Chapter 8 Chapter 9
Conformance Mining Additional Operational Support
Checking Perspectives
Part IV: Putting Process Mining to Work
Chapter 10 Chapter 11 Chapter 12
Tool Support Analyzing “Lasagna Analyzing “Spaghetti
Processes” Processes”
Part V: Reflection
Chapter 13 Chapter 14
Cartography and Epilogue
Navigation PAGE 28