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Chapter 6Advanced Process Discovery Techniquesprof.dr.ir. Wil van der Aalstwww.processmining.org
Overview
PAGE 1
Part I: Preliminaries
Chapter 2 Process Modeling and Analysis
Chapter 3Data Mining
Part II: From Event Logs to Process Models
Chapter 4 Getting the Data
Chapter 5 Process Discovery: An Introduction
Chapter 6 Advanced Process Discovery Techniques
Part III: Beyond Process Discovery
Chapter 7 Conformance Checking
Chapter 8 Mining Additional Perspectives
Chapter 9 Operational Support
Part IV: Putting Process Mining to Work
Chapter 10 Tool Support
Chapter 11 Analyzing “Lasagna Processes”
Chapter 12 Analyzing “Spaghetti Processes”
Part V: Reflection
Chapter 13Cartography and Navigation
Chapter 14Epilogue
Chapter 1 Introduction
Process discovery
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software system
(process)model
eventlogs
modelsanalyzes
discovery
records events, e.g., messages,
transactions, etc.
specifies configures implements
analyzes
supports/controls
enhancement
conformance
“world”
people machines
organizationscomponents
businessprocesses
Challenge
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process discovery
fitness
precisiongeneralization
simplicity
“able to replay event log” “Occam’s razor”
“not overfitting the log” “not underfitting the log”
Observing a stable process infinitely long
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trace in event log
frequent behavior
all behavior(including noise)
Target model
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target model
Non-fitting model
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non-fitting model
Overfitting model
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overfitting model
Underfitting model
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underfitting model
Characteristics of process discovery algorithms
• Representational bias− Inability to represent concurrency− Inability to deal with (arbitrary) loops− Inability to represent silent actions− Inability to represent duplicate actions− Inability to model OR-splits/joins− Inability to represent non-free-choice behavior− Inability to represent hierarchy
• Ability to deal with noise• Completeness notion assumed• Approach used (direct algorithmic approaches, two-
phase approaches, computational intelligence approaches, partial approaches, etc.)
PAGE 9
Examples
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• Algorithmic techniques• Alpha miner• Alpha+, Alpha++, Alpha#• FSM miner• Fuzzy miner• Heuristic miner• Multi phase miner
• Genetic process mining• Single/duplicate tasks• Distributed GM
• Region-based process mining• State-based regions• Language based regions
• Classical approaches not dealing with concurrency• Inductive inference (Mark Gold, Dana Angluin et al.)• Sequence mining
Heuristic mining
• To deal with noise and incompleteness.• To have a better representational bias than the α
algorithm (AND/XOR/OR/skip).• Uses C-nets.
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a
register claim
c e
close case
check damage
dconsult expert
b
check policy
Example log; problem α algorithm
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a
b
c
ed
p2
end
p4
p3p1
start
p5
Taking into account frequencies
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Dependency measure
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Example
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Lower threshold (2 direct successions and a dependency of at least 0.7)
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a c e
d
b
11(0.92)
11(0.92)
13(0.93)
5(0.83)
4(0.80)
13(0.93)
11(0.92)
11(0.92)
Higher threshold (5 direct successions and a dependency of at least 0.9)
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a c e
d
b11(0.92)
11(0.92)
13(0.93) 13(0.93)
11(0.92)
11(0.92)
Learning splits and joins
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a40
c21
e 40
d 17
b21
5
20
20
13
20
20
13
4
5
20
13
20
20 20
20
5
20
13 1313
4
4
Alternative visualization
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a40
c21
e 40
d 17
b21
5
20
20
13
20
20
13
4
5
20
13
20
20 20
20
5
20
13 1313
4
4
a c e
d
b
AND AND
Characteristics of heuristic mining
• Can deal with noise and therefore quite robust.• Improved representational bias.• Split and join rules are only considered locally
(therefore most of the discovered model are not sound and require repair actions).
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Genetic process mining
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next generationcomputefitness
elitism
parents
crossover
children
mutation
create initial population
“dead” individuals
tournament
select best individual
event log
termination
Design decisions
• Representation of individuals• Initialization• Fitness function• Selection strategy (tournament and elitism)• Crossover• Mutation
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next generationcomputefitness
elitism
parents
crossover
children
mutation
create initial population
“dead” individuals
tournament
select best individual
event log
termination
Example: crossover
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a
start register request
b
examine thoroughly
c
examine casually
d
check ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
a
start register request
b
examine thoroughly
c
examine casually
d
check ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
a
start register request
b
examine thoroughly
c
examine casually
d
check ticket
decide
reinitiate request
e
f
a
start register request
b
examine thoroughly
c
examine casually
d
check ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
pay compensation
reject request
g
hend
Example: mutation
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a
start register request
b
examine thoroughly
c
examine casually
d
check ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
a
start register request
b
examine thoroughly
c
examine casually
d
check ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
remove place
added arc
Characteristics of genetic process mining
• Requires a lot of computing power.• Can be distributed easily.• Can deal with noise, infrequent behavior, duplicate tasks,
invisible tasks, etc.• Allows for incremental improvement and combinations
with other approaches (heuristics post-optimization, etc.).PAGE 25
Region-based mining
• Two types of regions theory:− State-based regions− Language-based regions
• All about discovering places (like in the α algorithm)!
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a1
...
a2
am
b1
b2
bn
p(A,B) ...
A={a1,a2, … am} B={b1,b2, … bn}
State-based regions
Two steps:1.Discover a transition system (different abstractions
are possible)2.Convert transition system into an “equivalent” Petri
net.
PAGE 27
Step 1: learning a transition system
• past, future, past+future• sequence, multiset, set abstraction• limited horizon to abstract further• filtering e.g. based on transaction type, names, etc.• labels based on activity name or other features
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a b c d c d c d e f a g h h h ipast future
current state
past and future
trace:
Past without abstraction (full sequence)
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‹a,e,d›‹a,e›‹a›
‹a,b,c,d›
‹›
‹a,c,b,d›
‹a,b,c›‹a,b›
‹a,c,b›‹a,c›
a
c d
e d
b dc
b
Future without abstraction
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‹a,e,d› ‹d›
‹a,b,c,d›
‹›
‹a,c,b,d›
d
‹b,c,d›‹c,d›
‹c,b,d›‹b,d›
ea
a
a
b
bc
c
‹e,d›
Past with multiset abstraction
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[ ]
a
d
b dc
e
c
b
[a,b,c,d][a,b,c][a,c]
[a,b]
[a,e]
[a,d,e]
[a]
Only last event matters for state
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‹›a b
c
d
e d
d‹a›
‹b›
‹c›
‹d›
‹e›
c b
Step 2: constructing a Petri net using regions
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a
a
b
c
d
fpR
e
a
b
e
c
d
df
f
e
e
a = enterb = enterc = exitd = exite = do not crossf = do not cross
R
Example
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[ ]
a
d
b dc
e
cb
[a,b,c,d][a,b,c][a,c]
[ a,b][a,e] [a,d,e]
[a]
a
b
c
de
p2
end
p4
p3p1
start
Language based regions
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a1
a2
b1
b2
dpR
e
c1
c
f
YX
Region R = (X,Y,c) corresponding to place pR: X = {a1,a2,c1} = transitions producing a token for pR, Y = {b1,b2,c1} = transitions consuming a token from pR, and c is the initial marking of pR.
Based idea: enough tokens should be present when consuming
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a1
a2
b1
b2
dpR
e
c1
c
f
YX
A place is feasible if it can be added without disabling any of thetraces in the event log.
Example
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Regions
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Model
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b
c
a
e
d
p1 p2 p3 p4
p6
p5
Characteristics of region-based mining
• Can be used to discover more complex control-flow structures.
• Classical approaches need to be adapted (overfitting!).
• Representational bias can be parameterized (e.g., free-choice nets, label splitting, etc.).
• Problems dealing with noise.
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Other approaches, e.g. fuzzy mining
PAGE 41
Evaluating the discovered process
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Structure: Is this the simplest model (Occam's Razor)?
Fitness: Is the event log possible according to the model?
Precision: Is the model not underfitting (allow for too much)?
Generalization: Is the model not overfitting (only allow for the “accidental” examples)?
