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
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3. Process Mining: A bridge between data
mining and business process management
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4. Challenge: process discovery
Fitness: Is the event log
possible according to the
model?
Precision: Is the model Generalization: Is the model
not underfitting (allow for not overfitting (only allow for
too much)? the “accidental” examples)?
Structure: Is this the
simplest model (Occam's
Razor)?
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5. Challenge: supporting the whole
process mining spectrum
people organizations
machines
business “world”
processes documents
information system(s)
provenance
event logs
“pre “post
mortem” current historic mortem”
data data
navigation auditing cartography
recommend
diagnose
compare
enhance
discover
promote
explore
predict
detect
check
models
de jure models de facto models
control-flow control-flow
data/rules data/rules
resources/ resources/
organization organization
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6. Start Today!
• The threshold to start an (off-line) process mining
project is really low.
• Most organizations have event data hidden in their
systems.
• Once the data is located, conversion is typically easy.
For instance, software tools such as ProMimport, Nitro,
XESame, and OpenXES support the conversion of
different sources to MXML or XES.
• The freely available open-source process mining tool
ProM can be downloaded from www.processmining.org.
ProM can be applied to any MXML or XES file and
supports all of the process mining techniques
mentioned.
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7. Experience the “magic” of
process mining, i.e.,
discovering and improving
processes based on facts
rather than fiction!
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