distributed process discovery and conformance checking
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TRANSCRIPT
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Distributed Process Discovery and Conformance Checking
prof.dr.ir. Wil van der Aalstwww.processmining.org
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PAGE 1
On the different roles of (process) models …
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Play-Out
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Play-Out (Classical use of models)
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A B C D
A C B DA B C D
A E D
A C B DA C B D
A E D
A E D
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Play-In
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Play-In
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A C B DA B C D
A E D
A C B DA C B D
A E D
A E DA B C D
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Example Process Discovery(Vestia, Dutch housing agency, 208 cases, 5987 events)
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Example Process Discovery(ASML, test process lithography systems, 154966 events)
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Example Process Discovery(AMC, 627 gynecological oncology patients, 24331 events)
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Replay
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Replay
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A B C D
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Replay
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A E D
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Replay can detect problems
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AC D
Problem!missing token
Problem!token left behind
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Conformance Checking (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)
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Replay can extract timing information
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A5B8 C9 D13
5
8
9
13
3
4
5
43
265
8
764
7
74
3
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Performance Analysis Using Replay(WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)
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Big Data
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Big Data
PAGE 17
Source: “Big Data: The Next Frontier for Innovation, Competition, and Productivity” McKinsey Global Institute, 2011.
“Enterprises globally stored more than 7 exabytesof new data on disk drives in 2010, while consumers stored morethan 6 exabytes of new data on devices such as PCs and notebooks.”
“All of the world's music can be stored
on a $600 disk drive.”
“Indeed, we are generating so much data today that it is
physically impossible to store it all. Health
care providers, for instance, discard 90
percent of the data that they generate.”
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Hilbert and Lopez. The World's Technological Capacity to Store, Communicate, and Compute Information. Science, 332(6025):60-65, 2011.
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PAGE 19
www.olifantenpaadjes.nl
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PAGE 20
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PAGE 21
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Evidence-Based Computer Science
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PAGE 23
How good is my model?
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Four Competing Quality Criteria
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“able to replay event log” “Occam’s razor”
“not overfitting the log” “not underfitting the log”
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Example: one log four models
PAGE 25
astart register
request
bexamine thoroughly
cexamine casually
d checkticket
decide
pay compensation
reject request
reinitiate requeste
g
hfend
astart register
request
cexamine casually
dcheckticket
decide reject request
e hend
N3 : fitness = +, precision = -, generalization = +, simplicity = +
N2 : fitness = -, precision = +, generalization = -, simplicity = +
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
N1 : fitness = +, precision = +, generalization = +, simplicity = +
astart register
request
cexamine casually
dcheckticket
decide reject request
e hend
N4 : fitness = +, precision = +, generalization = -, simplicity = -
aregister request
dexamine casually
ccheckticket
decide reject request
e h
a cexamine casually
dcheckticket
decide
e g
a dexamine casually
ccheckticket
decide
e g
register request
register request
pay compensation
pay compensation
aregister request
b dcheckticket
decide reject request
e h
aregister request
d bcheckticket
decide reject request
e h
a b dcheckticket
decide
e gregister request
pay compensation
examine thoroughly
examine thoroughly
examine thoroughly
(all 21 variants seen in the log)
acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
process discovery
fitness
precisiongeneralization
simplicity
“able to replay event log” “Occam’s razor”
“not overfitting the log” “not underfitting the log”
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Model N1
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acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
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Model N2
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acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
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Model N3
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acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
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Model N4
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acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
astart register
request
cexamine casually
dcheckticket
decide reject request
e hend
N4 : fitness = +, precision = +, generalization = -, simplicity = -
aregister request
dexamine casually
ccheckticket
decide reject request
e h
a cexamine casually
dcheckticket
decide
e g
a dexamine casually
ccheckticket
decide
e g
register request
register request
pay compensation
pay compensation
aregister request
b dcheckticket
decide reject request
e h
aregister request
d bcheckticket
decide reject request
e h
a b dcheckticket
decide
e gregister request
pay compensation
examine thoroughly
examine thoroughly
examine thoroughly
(all 21 variants seen in the log)
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Process Discovery
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Process Discovery (small selection)
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α algorithm
α++ algorithm
α# algorithm
language-based regions
state-based regionsgenetic mining
heuristic mining
hidden Markov models
neural networks
automata-based learning
stochastic task graphs
conformal process graph
mining block structures
multi-phase miningpartial-order based mining
fuzzy mining
LTL mining
ILP mining
distributed genetic mining
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Petri net view: Just discover the places …
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Adding a place limits behavior:• overfitting ≈ adding too many places • underfitting ≈ adding too few places
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Example: Process Discovery UsingState-Based Regions
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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
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Example of Region
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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
enter: b,eleave: ddo-not-cross: a,c
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Example: Process Discovery UsingLanguage-Based Regions
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R
A place is feasible if it can be added without disabling any of thetraces in the event log.
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Conformance Checking
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Replaying trace “abeg”
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m=1r=1
a b e g
6
11
6= 0.83333
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Can be lifted to log level
PAGE 38
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
p3p1
p2 p4
N1
p5
acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
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From “playing the token game” to optimal alignments …
a b » e ga b d e g
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observed trace: “abeg”
move in model only
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Another alignment
a b c d e ga b » d e g
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observed trace: “abcdeg”
move in log only
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Moves in an alignment
PAGE 41
a b » d e ga » c d e g
trace in event log
possible run of model
move in log
move in model move in both
Optimal alignment describes modeled behavior closest to observed behavior
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Moves have costs
• Standard cost function:
−c(x,») = 1−c(»,y) = 1−c(x,y) = 0, if x=y−c(x,y) = ∞, if x≠y
PAGE 42
… a …… » …
… » …… a …
… a …… a …
… a …… b …
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Non-fitting trace: abefdeg
PAGE 43
abefdeg
a b » e f d » e ga b d e f d b e g 2
2a b e f d e ga b » » d e g
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Any cost structure is possible
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… send-letter(John,2 weeks, $400)
…
… send-email(Sue,3weeks,$500)
…
• Similar activities (more similarity implies lower costs).• Resource conformance (done by someone that does
not have the specified role).• Data conformance (path is not possible for this
customer). • Time conformance (missed the legal deadline)
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Fitness
PAGE 45
astart register
request
bexamine thoroughly
cexamine casually
d checkticket
decide
pay compensation
reject request
reinitiate requeste
g
hfend
astart register
request
cexamine casually
dcheckticket
decide reject request
e hend
N3 : fitness = +, precision = -, generalization = +, simplicity = +
N2 : fitness = -, precision = +, generalization = -, simplicity = +
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
N1 : fitness = +, precision = +, generalization = +, simplicity = +
astart register
request
cexamine casually
dcheckticket
decide reject request
e hend
N4 : fitness = +, precision = +, generalization = -, simplicity = -
aregister request
dexamine casually
ccheckticket
decide reject request
e h
a cexamine casually
dcheckticket
decide
e g
a dexamine casually
ccheckticket
decide
e g
register request
register request
pay compensation
pay compensation
aregister request
b dcheckticket
decide reject request
e h
aregister request
d bcheckticket
decide reject request
e h
a b dcheckticket
decide
e gregister request
pay compensation
examine thoroughly
examine thoroughly
examine thoroughly
(all 21 variants seen in the log)
acdeh
abdeg
adceh
abdeh
acdeg
adceg
adbeh
acdefdbeh
adbeg
acdefbdeh
acdefbdeg
acdefdbeg
adcefcdeh
adcefdbeh
adcefbdeg
acdefbdefdbeg
adcefdbeg
adcefbdefbdeg
adcefdbefbdeh
adbefbdefdbeg
adcefdbefcdefdbeg
455
191
177
144
111
82
56
47
38
33
14
11
9
8
5
3
2
2
1
1
1
# trace
1391
1.0
0.8
1.0
1.0
Our A* algorithm exploits the Petri net marking equation and uses other “tricks” to prune the search space.
Aligned event log is starting point for other types of analysis.
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PAGE 46
Distributing/Decomposing “Big Data” Process Mining Problems
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PAGE 47
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What if?
PAGE 48
book car
c
add extra insurance
d change booking
e
confirm initiate check-in
j
check driver’s license
k
charge credit card
i
select car
g
supply car
in
a
b
skip extrainsurance
f
h
add extra insurance
skip extra insurance
l
out
c1 c2
c3
c4
c5
c6
c7
c8
c9
c10
c11
acefgijklacddefhkjilabdefjkgil
acdddefkhijlacefgijklabefgjikl
...
processdiscovery
conformance checking
there are more than 1000 different
activities?
there are more than 1.000.000
cases?
there are more than 100.000.000
events?
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Distributed computing
• multicore CPU• manycore GPU• cluster computing• grid computing• cloud computing• …
PAGE 49
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How to distribute process discovery?
PAGE 50
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How to distribute conformance checking?
PAGE 51
abcdegadcefbcfdeg
abdcegabcdefbcdegabdfcefdcegacdefbdceg
abcdegabdceg
abdcefbdcefbdcegabcdeg
abcdefbcdefbdcegabcdefbdceg
acdefgadcfeg
abdcefcdfegabcdeg
a b
c
d
e
c1in
c2
c3
c4
c5
g
outc6
f
abcdegabdceg
abcdefbcdegabcdegabdceg
abdcefbdcefbdcegabcdeg
abcdefbcdefbdcegabcdefbdceg
abcdeg
a b
c
d
e
c1in
c2
c3
c4
c5
g
outc6
f
adcefbcfdegabdfcefdcegacdefbdceg
acdefgadcfeg
abdcefcdfeg
b is often skipped
f occurs too often
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Classification based on partitioning of event log: vertical and horizontal
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sets of cases
sets of activities
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Replication: Same event log on all computing nodes
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Only makes sense if random elements, e.g., genetic process mining.
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Vertical distribution I: Split cases arbitrarily
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abcdegabdcefbcdeg
abdcegabcdefbcdegabdcefbdcegabcdefbdceg
abcdegabdceg
abdcefbdcefbdcegabcdeg
abcdefbcdefbdcegabcdefbdceg
abcdegabdceg
abdcefbcdegabcdeg
abcdegabdcefbcdeg
abdcegabcdefbcdegabdcefbdcegabcdefbdceg
abcdegabdceg
abdcefbdcefbdcegabcdeg
abcdefbcdefbdcegabcdefbdceg
abcdegabdceg
abdcefbcdegabcdeg
sets of cases
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Vertical distribution II: Split cases based on a specific feature
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abcdegabdcefbcdeg
abdcegabcdefbcdegabdcefbdcegabcdefbdceg
abcdegabdceg
abdcefbdcefbdcegabcdeg
abcdefbcdefbdcegabcdefbdceg
abcdegabdceg
abdcefbcdegabcdeg
abcdegabdcegabcdegabdcegabcdegabcdegabdcegabcdeg
abdcefbcdegabcdefbcdegabdcefbdcegabcdefbdcegabcdefbdcegabdcefbcdeg
abdcefbdcefbdcegabcdefbcdefbdceg
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Horizontal distribution
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sets of activities
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Horizontal distribution: The key idea
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projected on a,b,e,f,g
projected on b,c,d,e
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Passages for Horizontal Distribution
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Passages
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Passage P=(X,Y)
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causal dependency: may trigger or enable
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Minimal passages
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a passage is minimal if it does not contain smaller passages
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Passages define an equivalence relation on the edges in the graph
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Minimal passage 1: ({a},{b,c})
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Minimal passage 2: ({b,c,d},{d,e,f})
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Minimal passage 3: ({e},{g})
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Minimal passage 4: ({f},{h})
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Minimal passage 5: ({g,h},{i})
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So What?
• Any process model can be partitioned in minimal passages.
• Discovery and conformance checking can be done per passage!
PAGE 68
b
a
c
d
e
j
i
hf nk
l
m
o
pgi o
clouds may contain arbitrary subprocesses not explicitly recorded in the
event log (invisible activities or small networks used for routing, e.g. XOR/AND/OR-
split/joins)
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Example result for Petri nets
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“The event log fits all passages if and only if the event log fits the whole model.”
b
a
c
d
e
j
i
hf nk
l
m
o
pgi o
Key insight: interface transitions controlled by event log
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causal structure obtained using heuristics & domain knowledge
Discovery example
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a
in
g
out
b
f
a
b
c
d
c
d
e
f
e
g
a b
c
d
e
c1in
c2
c3
c4
c5
g
outc6
f
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Conformance checking
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book car
c
add extra insurance
d change booking
e
confirm initiate check-in
j
check driver’s license
k
charge credit card
i
select car
g
supply car
in
a
b
skip extrainsurance
f
h
add extra insurance
skip extra insurance
l
out
c1 c2
c3
c4
c5
c6
c7
c8
c9
c10
c11
aceflacddeflabdefl
acdddeflaceflabefl
...
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Create Skeleton
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Net fragments per passage
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Initial implementation in ProM
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Super linear speedups possible (even when using a single computer decomposition helps)
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Conclusion
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Conclusion
PAGE 77“Big Data”
b
a
c
d
e
j
i
hf nk
l
m
o
pgi o
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PAGE 78
www.processmining.org
www.win.tue.nl/ieeetfpm/