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. Productivity improvements
• Adam Smith (1723-1790) showed the advantages of
the division of labor.
• Frederick Taylor (1856-1915) introduced the initial
principles of scientific management.
• Henry Ford (1863-1947) introduced the production
line for the mass production of “black T-Fords”.
• Since 1950 computers and digital communication
infrastructures are the most dominant factor
influencing business processes and their
management.
PAGE 2
4. Role of models
• Operations management, and in particular operation
research, is a branch of management science heavily
relying on modeling.
• Models are used to reason about processes
(redesign) and to make decisions inside processes
(planning and control).
• A process model may be used to discuss
responsibilities, analyze compliance, predict
performance using simulation, and configure a WFM
system.
PAGE 3
5. Problems of models
• The model describes an idealized version of reality.
• Inability to adequately capture human behavior.
• The model is at the wrong abstraction level.
• Therefore, we advocate the use of event data:
− Process mining allows for the extraction of models based on
facts.
− Moreover, process mining does not aim at creating a single
model of the process.
− Instead, it provides various views on the same reality at
different abstraction levels.
− For example, users can decide to look at the most frequent
behavior to get a simple model (“80% model”).
− However, they can also inspect the full behavior by deriving the
“100% model” covering all cases observed. PAGE 4
7. Petri nets
XOR-split XOR-join XOR-split
b
AND-split examine AND-join
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
XOR-join t transition
AND-split
p place
token
PAGE 6
8. Reachability graph
XOR-split XOR-join XOR-split
b
AND-split examine AND-join
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
XOR-join t transition
AND-split
p place
token
examine pay
reinitiate
thoroughly compensation
request
register examine check reject
request casually ticket [c5] request
[start] [c1,c2] [c2,c3] [end]
check decide
ticket examine
casually
[c1,c4] [c3,c4]
examine
thoroughly PAGE 7
9. How many states?
i1 t1
i2 t2
t t
i3 t3 out
i4 t4
p p
i5 t5
(a) (b) (c)
PAGE 8
11. condition (like a place in a Petri net)
YAWL task (i.e., an atomic activity)
AND-split AND-join
XOR-split XOR-join
OR-split OR-join
start end
multiple composite
instance task task
examine thoroughly
cancelation
pay region
OR-split OR-join compensation
register
request c1
examine
casually
start decide end
check
c2 ticket reject
request
new
information
PAGE 10
c3
12. task/activity
AND-split AND-join
BPMN gateway gateway
XOR-split XOR-join
gateway gateway
OR-split OR-join
gateway gateway
start end
event event
intermediate events
y
x
z
examine
thoroughly deferred choice pattern using the event-based XOR gateway
pay
examine
compensation
casually
register
decide
request
start reject end
check ticket
request
reinitiate
request
PAGE 11
13. Event-Driven Process Chains (EPCs)
function
AND-split AND-join
AND AND
connector connector
XOR-split XOR-join
XOR XOR
connector connector
OR-split OR-join
OR OR
connector connector
start end
event event
intermediate event
examine
e1
thoroughly
start OR OR e4 pay
e5 end
compensation
examine
e2
casually
register
XOR AND AND decide
request
check
e3
ticket reject
XOR e6 end
request
PAGE 12
15. Causal nets (C-nets)
b
examine
thoroughly
g
pay
c
compensation
a e z
examine
register casually decide end
request
h
d
reject
check request
ticket
f
reinitiate
request
XOR-split AND-split OR-split
XOR-join AND-join OR-join
PAGE 14
16. Why C-nets?
• Similar to heuristic nets and representation used by
genetic miners.
• Fits well with mainstream languages (BPMN, EPCs,
YAWL, BPEL, etc.).
• Model XOR, AND, and OR, but no silent steps or
duplicate activities needed.
• Loose interpretation (focus on replay semantics
rather than execution semantics).
PAGE 15
17. Another C-net
b
book
flight
a c e
start book complete
booking car booking
d
book
hotel
PAGE 16
18. WF-net interpretation of C-nets
(only valid sequences!)
b
book
flight
a c e
start book complete
booking car booking
d
book
hotel
b
book
flight
a c e
start book complete
booking car booking
d
book
hotel
PAGE 17
19. b
book
Non-sound C-nets flight
a c e
start book complete
booking car booking
d
book
hotel
(a) unsound because there are no valid sequences
b
book
flight
a c e
start book complete
booking car booking
d
book
hotel
PAGE 18
(b) unsound although there exist valid sequences
21. Verification
b
examine deadlock
thoroughly
g
c1 c3 pay
c compensation
a examine c6 e
start register casually decide c5 end
request
h
c2 d c4 reject
check ticket request
f
reinitiate
request
PAGE 20
24. Limitations of model-based analysis
• Verification and performance analysis heavily rely on
the availability of high quality models.
• When the models and reality have little in common,
model-based analysis does not make much sense.
• There is often a lack of alignment between hand-
made models and reality
• Process mining aims to address these problems by
establishing a direct connection between the models
and actual low-level event data about the process.
• Process discovery techniques allow for viewing the
same reality from different angles and at different
levels of abstraction.
PAGE 23
