Process MiningUnderstanding and Improving Desire Lines in Big Data
prof.dr.ir. Wil van der Aalstwww.processmining.org
PAGE 2
Let’s Play: Play-Out, Play-In, ReplayBig DataDesire LinesProcess MiningHow Good is My Model?
Process DiscoveryConformance Checking
Food for Thought: Lasagna and SpaghettiGoogle Maps and TomTom
How to Get Started?Conclusion
PAGE 3
On the different roles of (process) models …
Play-Out
PAGE 4
event logprocess model
A
B
C
DE
p2
end
p4
p3p1
start
Play-Out (Classical use of models)
PAGE 5
A B C D
A C B DA B C D
A E D
A C B D
A C B D
A E D
A E D
Play-In
PAGE 6
event log process model
A
B
C
DE
p2
end
p4
p3p1
start
Play-In
PAGE 7
A C B DA B C D
A E D
A C B D
A C B D
A E D
A E DA B C D
Example Process Discovery(Vestia, Dutch housing agency, 208 cases, 5987 events)
PAGE 8
Example Process Discovery(ASML, test process lithography systems, 154966 events)
PAGE 9
Example Process Discovery(AMC, 627 gynecological oncology patients, 24331 events)
PAGE 10
Replay
PAGE 11
event log process model
· extended model showing times, frequencies, etc.
· diagnostics· predictions· recommendations
A
B
C
DE
p2
end
p4
p3p1
start
Replay
PAGE 12
A B C D
A
B
C
DE
p2
end
p4
p3p1
start
Replay
PAGE 13
A E D
A
B
C
DE
p2
end
p4
p3p1
start
Replay can detect problems
PAGE 14
AC D
Problem!missing token
Problem!token left behind
Conformance Checking (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)
PAGE 15
A
B
C
DE
p2
end
p4
p3p1
start
Replay can extract timing information
PAGE 16
A5B8 C9 D13
5
8
9
13
3
4
5
43
265
8
764
7
74
3
PAGE 17
Performance Analysis Using Replay(WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)
PAGE 18
Big Data
Big Data
PAGE 19
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.”
PAGE 20
Hilbert and Lopez. The World's Technological Capacity to Store, Communicate, and Compute Information. Science, 332(6025):60-65, 2011.
PAGE 21
www.olifantenpaadjes.nl
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Evidence-Based Business Process Management
PAGE 25
PAGE 26
PAGE 27
Process Mining
PAGE 28
Event Data + Processes
Process Mining =
Data Mining + Process Analysis
Machine Learning + Formal Methods
Process Mining
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
Starting point: event log
PAGE 30
XES, MXML, SA-MXML, CSV, etc.
Simplified event log
PAGE 31
a = register request, b = examine thoroughly, c = examine casually, d = check ticket,e = decide, f = reinitiate request, g = pay compensation, and h = reject request
Processdiscovery
PAGE 32
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
c1
c2
c3
c4
c5
Conformance checking
PAGE 33
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
c1
c2
c3
c4
c5
case 7: e is executed without being
enabled
case 8: g or h is missing
case 10: e is missing in second
round
Extension: Adding perspectives to model based on event log
PAGE 34
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
c1
c2
c3
c4
c5
Performance information (e.g., the average time between two subsequent activities) can be extracted from the event log and visualized on top of the model.
A
A
A
A
A
E
M
M
Pete
Mike
Ellen
Role A:Assistant
Sue
Sean
Role E:Expert
Sara
Role M:Manager
Decision rules (e.g., a decision tree based on data known at the time a particular choice was made) can be learned from the event log and used to annotated decisions.
The event log can be used to discover roles in the organization (e.g., groups of people with similar work patterns). These roles can be used to relate individuals and activities.
PAGE 35
How good is my model?
Four Competing Quality Criteria
PAGE 36
process discovery
fitness
precisiongeneralization
simplicity
“able to replay event log” “Occam’s razor”
“not overfitting the log” “not underfitting the log”
Example: one log four models
PAGE 37
astart register
request
bexamine thoroughly
cexamine casually
d checkticket
decide
pay compensation
reject request
reinitiate requeste
g
hf
end
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”
Model N1
PAGE 38
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
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
N1 : fitness = +, precision = +, generalization = +, simplicity = +
Model N2
PAGE 39
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
N2 : fitness = -, precision = +, generalization = -, simplicity = +
Model N3
PAGE 40
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
bexamine thoroughly
cexamine casually
d checkticket
decide
pay compensation
reject request
reinitiate requeste
g
hf
end
N3 : fitness = +, precision = -, generalization = +, simplicity = +
Model N4
PAGE 41
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)
PAGE 42
Process Discovery
Process Discovery (small selection)
PAGE 43
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
Petri net view: Just discover the places …
PAGE 44
a1
...
a2
am
b1
b2
bn
p(A,B) ...
A={a1,a2, … am} B={b1,b2, … bn}
process discovery
fitness
precisiongeneralization
simplicity
“able to replay event log” “Occam’s razor”
“not overfitting the log” “not underfitting the log”
Adding a place limits behavior:•overfitting ≈ adding too many places •underfitting ≈ adding too few places
Example: Process Discovery UsingState-Based Regions
PAGE 45
[ ]
a
d
b d
c
e
c
b
[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
event log
01011001101101001011111101101000110110011110111000001101101001001100
Example of State-Based Region
PAGE 46
[ ]
a
d
b d
c
e
c
b
[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
Example: Process Discovery UsingLanguage-Based Regions
PAGE 47
a1
a2
b1
b2
d
pR
e
c1
c
f
YX
A place is feasible if it can be added without disabling any of thetraces in the event log.
Example of Language-Based Regions
PAGE 48
b
a
d
e
c
YX
1. accd
2. bd
3. bce
4. ace
5. acd
6. bcce
7. ade
↓accd : 0 + 0 - 0 ≥ 0
a↓ccd : 0 + 1 - 1 ≥ 0
ac↓cd : 0 + 2 - 2 ≥ 0
acc↓d : 0 + 3 - 3 ≥ 0
↓ade : 0 + 0 - 0 ≥ 0
a↓de : 0 + 1 - 1 ≥ 0
ad↓e : 0 + 1 - 2 < 0
Example of a completely different process discovery technique: Genetic Mining
PAGE 49
Genetic process mining: Overview
PAGE 50
next generationcomputefitness
elitism
parents
crossover
children
mutation
create initial population
“dead” individuals
tournament
select best individual
event log
termination
Example: crossover
PAGE 51
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
h
end
Example: mutation
PAGE 52
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
PAGE 53
Conformance Checking
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
Replaying trace “abeg”
PAGE 54
m=1
r=1
a b e g
6
11
6= 0.83333
Can be lifted to log level
PAGE 55
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
astart register
request
bexamine
thoroughly
cexamine casually
checkticket
decide
pay compensation
reject request
reinitiate request
e
g
hf
endp1 p2
N2
dp3
p4
astart register
request
cexamine casually
dcheckticket
decide reject request
e hend
p1
p2
p3
p4
p5
N3
astart register
request
bexamine thoroughly
cexamine casually
d checkticket
decide
pay compensation
reject request
reinitiate requeste
g
hf
end
N4
p1
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
From “playing the token game” to optimal alignments …
a b » e g
a b d e g
PAGE 56
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
observed trace: “abeg”
move in model only
Another alignment
a b c d e g
a b » d e g
PAGE 57
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end
observed trace: “abcdeg”
move in log only
Moves in an alignment
PAGE 58
a b » d e g
a » 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
Moves have costs
• Standard cost function:
−c(x,») = 1
−c(»,y) = 1
−c(x,y) = 0, if x=y
−c(x,y) = ∞, if x≠yPAGE 59
… a …
… » …… » …
… a …
… a …
… a …
… a …
… b …
Non-fitting trace: abefdeg
PAGE 60
astart register
request
bexamine
thoroughly
cexamine casually
dcheck ticket
decide
pay compensation
reject request
reinitiate request
e
g
h
f
end abefdeg
a b » e f d » e g
a b d e f d b e g2
2a b e f d e g
a b » » d e g
Any cost structure is possible
PAGE 61
… send-letter(John,2 weeks, $400)
…
… send-email(Sue,3 weeks,$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)
Fitness
PAGE 62
astart register
request
bexamine thoroughly
cexamine casually
d checkticket
decide
pay compensation
reject request
reinitiate requeste
g
hf
end
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.
PAGE 63
Alignments are essential!•conformance checking to diagnose deviations•squeezing reality into the model to do model-based analysis
PAGE 64
Food for Thought
We applied ProM in >100 organizations
PAGE 65
• Municipalities (e.g., Alkmaar, Heusden, Harderwijk, etc.)• Government agencies (e.g., Rijkswaterstaat, Centraal
Justitieel Incasso Bureau, Justice department)• Insurance related agencies (e.g., UWV)• Banks (e.g., ING Bank)• Hospitals (e.g., AMC hospital, Catharina hospital)• Multinationals (e.g., DSM, Deloitte)• High-tech system manufacturers and their customers
(e.g., Philips Healthcare, ASML, Ricoh, Thales)• Media companies (e.g. Winkwaves)• ...
How can process mining help?
PAGE 66
• Uncover bottlenecks• Detect deviations• Performance
measurement• Auditing/compliance• Business Process
Redesign (BPR)• Continuous improvement
(Six Sigma)• Operational support (e.g.,
recommendation and prediction)
• Provide new insights• Highlight important
problems• An organization’s
mirror (in two ways)• Helps to avoid ICT
failures• Avoid “management by
PowerPoint” • From “politics” to
“analytics”
PAGE 67
Example of a Lasagna process: WMO process of a Dutch municipality
PAGE 68
Each line corresponds to one of the 528 requests that were handled in the period from 4-1-2009 until 28-2-2010. In total there are 5498 events represented as dots. The mean time needed to handled a case is approximately 25 days.
WMO process(Wet Maatschappelijke Ondersteuning)
• WMO refers to the social support act that came into force in The Netherlands on January 1st, 2007.
• The aim of this act is to assist people with disabilities and impairments. Under the act, local authorities are required to give support to those who need it, e.g., household help, providing wheelchairs and scootmobiles, and adaptations to homes.
• There are different processes for the different kinds of help. We focus on the process for handling requests for household help.
• In a period of about one year, 528 requests for household WMO support were received.
• These 528 requests generated 5498 events.PAGE 69
C-net discovered using heuristic miner (1/3)
PAGE 70
C-net discovered using heuristic miner (2/3)
PAGE 71
C-net discovered using heuristic miner (3/3)
PAGE 72
Conformance check WMO process (1/3)
PAGE 73
Conformance check WMO process (2/3)
PAGE 74
Conformance check WMO process (3/3)
PAGE 75
The fitness of the discovered process is 0.99521667. Of the 528 cases, 496 cases fit perfectly whereas for 32 cases there are missing or remaining tokens.
Bottleneck analysis WMO process (1/3)
PAGE 76
Bottleneck analysis WMO process (2/3)
PAGE 77
Bottleneck analysis WMO process (3/3)
PAGE 78
flow time of approx. 25 days with a standard deviation of approx. 28
Two additional Lasagna processes
PAGE 79
RWS (“Rijkswaterstaat”)
process
WOZ (“Waardering Onroerende Zaken”)
process
RWS Process
PAGE 80
• The Dutch national public works department, called “Rijkswaterstaat” (RWS), has twelve provincial offices. We analyzed the handling of invoices in one of these offices.
• The office employs about 1,000 civil servants and is primarily responsible for the construction and maintenance of the road and water infrastructure in its province.
• To perform its functions, the RWS office subcontracts various parties such as road construction companies, cleaning companies, and environmental bureaus. Also, it purchases services and products to support its construction, maintenance, and administrative activities.
C-net discovered using heuristic miner
PAGE 81
Social network constructed based on handovers of work
PAGE 82
Each of the 271 nodes corresponds to a civil servant. Two civil servants areconnected if one executed an activity causally following an activity executed by the other civil servant
Social network consisting of civil servants that executed more than 2000 activities in a 9 month period.
PAGE 83
The darker arcs indicate the strongest relationships in the social network. Nodes having the same color belong to the same clique.
WOZ process
• Event log containing information about 745 objections against the so-called WOZ (“Waardering Onroerende Zaken”) valuation.
• Dutch municipalities need to estimate the value of houses and apartments. The WOZ value is used as a basis for determining the real-estate property tax.
• The higher the WOZ value, the more tax the owner needs to pay. Therefore, there are many objections (i.e., appeals) of citizens that assert that the WOZ value is too high.
• “WOZ process” discovered for another municipality (i.e., different from the one for which we analyzed the WMO process).
PAGE 84
Discovered process model
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The log contains events related to 745 objections against the so-called WOZ valuation. These 745 objections generated 9583 events. There are 13 activities. For 12 of these activities both start and complete events are recorded. Hence, the WF-net has 25 transitions.
Conformance checker:(fitness is 0.98876214)
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Performance analysis
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bottleneck detection: places are colored based on average durations
information on total flow time
time required to move from one activity to another
Resource-activity matrix(four groups discovered)
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clique 2
clique 1
clique 3
clique 4
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Example of a Spaghetti process
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Spaghetti process describing the diagnosis and treatment of 2765 patients in a Dutch hospital. The process model was constructed based on an event log containing 114,592 events. There are 619 different activities (taking event types into account) executed by 266 different individuals (doctors, nurses, etc.).
Fragment18 activities of the 619 activities (2.9%)
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Another example(event log of Dutch housing agency)
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The event log contains 208 cases that generated 5987 events. There are 74 different activities.
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Google Maps and TomTom
Process models should be treated as electronic maps
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Business process movies
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Business information systems should offer “TomTom” functionality
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Predict: When will I be home? At 11.26!
Recommend: How to get home ASAP? Take a left turn!
Detect: You drive too fast!
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How to get started?
Hundreds of plug-ins available covering the whole process mining spectrum
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open-source (L-GPL)
How to Get Started?
Collect event data• Minimal requirement:
events referring to an activity name and a process instance.
• Good to have: timestamps, resource information, additional data elements.
• Challenges: scoping and sometimes correlation.
Collect questions
• What kind problems would you like to address (cost, time, risk, compliance, service, etc.)?
• Related to discovery, conformance, enhancement?
• Iterative process: can be “curiosity driven” initially.
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Conclusion
Conclusion
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www.processmining.org
www.win.tue.nl/ieeetfpm/