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Block-Structured Process Discovery:
Filtering Infrequent Behaviour
Sander Leemans
Dirk Fahland
Wil van der Aalst
Eindhoven University of Technology
Process discovery
Fast
Fitting
Precise
General
Simple
Sound
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Trade-off
not fitting not simple
not sound not sound
a
b
c
α ILP
Flower model
τ
not precise Heuristics τMiner Evolutionary
Tree Miner
f
d
e
not fitting
not sound not fast
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Infrequent behaviour
• 80% model
• Filtering beforehand is
difficult
• Filter during discovery
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Outline
?
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Process trees
a
τ
τ
b
τ
x
c
d
τ
e
/\
a
b
→
c
e
d
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Outline
?
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Divide & conquer
a
{<a>,
<a>,
<a>,
<a>}
{<a,c,d,e,b>,
<a,b,e,d,c>,
<a,e,c,b,d>,
<a,d,b,c,e>}
recurse
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{<c,d,e,b>,
<b,e,d,c>,
<e,c,b,d>,
<d,b,c,e>}
recurse
8
Finding operator
a
{<a,c,d,e,b>,
<a,b,e,d,c>,
<a,e,c,b,d>,
<a,d,b,c,e>}
a
b
c
d
e
{<c,d,e,b>,
<b,e,d,c>,
<e,c,b,d>,
<d,b,c,e>}
• Find cut in directly-follows graph
recurse
• Sequence: edges crossing oneway only
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Inductive Miner
• Divide activities, select operator
• Split log
• Recurse until base case
?
{a,b}
{c,d}
?
{c}
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{d}
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Outline
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Inductive Miner - infrequent
• Divide activities, select operator
• Split log
• Recurse until base case
?
{a,b}
{c,d}
?
Threshold
{c}
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{d}
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Divide activities, select operator
• Filter infrequent edges
(b,c) 100
(b,d) 100
(b,e) 100
(b,a) 1
a
100
1
100
100
100
d
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b
100
c
100
100
100
100
e
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Divide activities, select operator
• Weaker log
(b,c)
1
• Use
eventually-follows
(b,d) 1instead
relation
(b,e) 1 “correct”
• Amplifies
(b,a) 1
edges
a
14
15
1
5
1
<a, b, c>
a
b
1
5
1
2
1
2
d
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c
1
2
1
2
c
1
2
b
1
2
e
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Split log
{a}
<a,a,a,b,b,b>
<a,a,b,b,a,b>
{b}
<a,a,a>
<b,b,b>
<a,a>
<b,b,b>
<a,a,a>
<b>
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Outline
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Comparison
• 12 logs, 5 miners
• Discover (< 2 hours)
• (Convert to Petri net)
• Measure
• Inductive Miner
• Inductive Miner infrequent
• Heuristics Miner
• Integer Linear
Programming Miner
• Evolutionary Tree Miner
• (Flower model)
• (Trace model)
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Comparison
fitness
soundness
precision
IM
IMi
HM
completed
generalisation
mining time
ILP
ETM
simplicity
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Comparison
fitness
soundness
precision
IM
IMi
HM
completed
generalisation
mining time
ILP
ETM
simplicity
Sander Leemans
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Comparison
fitness
soundness
precision
IM
IMi
HM
completed
generalisation
mining time
ILP
ETM
simplicity
Sander Leemans
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Comparison
fitness
soundness
precision
IM
IMi
HM
completed
generalisation
mining time
ILP
ETM
simplicity
Sander Leemans
21
Comparison
fitness
soundness
precision
IM
IMi
HM
completed
generalisation
mining time
ILP
ETM
simplicity
Sander Leemans
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Comparison
fitness
soundness
precision
IM
IMi
HM
completed
generalisation
mining time
ILP
ETM
simplicity
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You have been watching
In order of appearance
?
?
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Base cases
• Enough empty traces:
filter
< >1000
<a>1000
<a,a>1
• Average number of
events per trace close
enough to 1
< >1
<a>1000
<a,a>1
< >1000
<a>1000
<a,a>1000
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x
<a>1000
<a,a>1
τ
a
a
τ
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