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Process Mining: Discovering and Improving Spaghetti and Lasagna Processes prof.dr.ir. Wil van der Aalst www.processmining.org

Keynote on Process Mining at SSCI 2010 / CIDM 2011

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Keynote IEEE Symposium Series on Computational Intelligence (SSCI 2011)/IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), April 2011, Paris, France

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Page 1: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Process Mining: Discovering and Improving Spaghetti and Lasagna Processes

prof.dr.ir. Wil van der Aalstwww.processmining.org

Page 2: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Architecture of Information Systems @ TU/e

Process Mining

PAIS Technology

Process Modeling/Analysis

process discovery

conformance checking

verification

simulation

workflow patterns

BPM/WFM/SOA systems

Page 3: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Data explosion

PAGE 2

Page 4: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 3

The World's Technological Capacity to Store, Communicate, and Compute Information by Martin Hilbert and Priscila López (DOI 10.1126/science.1200970)

Page 5: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 4Data Mining

Smoker

Drinker

Weight

Short(91/10)

YesNo

Long(30/1)

NoYes

Long(150/20)

Short(321/25)

<81.5 ≥81.5

Process Mining =

Process Analysis

start register initial conditions

check_Aneeded?

check_A

modify conditions

check_Bneeded?

check_B

check_Cneeded?

check_C

assesrisk

declinec1

c2

c3

c4

c5

c6

c7

c8

c9

c10

c11

c12

c13

makeoffer

handleresponse

handlepayment

send insurance

documents

timeout1 timeout2 withdraw offer

c14 c15 c16

c17

(RM,RD)(RM,RD)(E,SD) (E,RD)

(SM,SD) (E,SD)(E,FD)

(E,SD)

(E,SD)

(YE,RD)

(YE,RD)

(FE,FD)

(RM,RD)

+

Page 6: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 5

Process Mining

• Process discovery: "What is really happening?"

• Conformance checking: "Do we do what was agreed upon?"

• Performance analysis: "Where are the bottlenecks?"

• Process prediction: "Will this case be late?"

• Process improvement: "How to redesign this process?"

• Etc.

Page 7: Keynote on Process Mining at SSCI 2010 / CIDM 2011

We applied ProM in >100 organizations

PAGE 6

• 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)• ...

Page 8: Keynote on Process Mining at SSCI 2010 / CIDM 2011

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

Page 9: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Starting point: event log

PAGE 8

XES, MXML, SA-MXML, CSV, etc.

Page 10: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Simplified event log

PAGE 9

a = register request, b = examine thoroughly, c = examine casually, d = check ticket,e = decide, f = reinitiate request, g = pay compensation, and h = reject request

Page 11: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Processdiscovery

PAGE 10

astart register

request

bexamine

thoroughly

cexamine casually

d

check ticket

decide

pay compensation

reject request

reinitiate request

e

g

h

f

end

c1

c2

c3

c4

c5

Page 12: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Conformance checking

PAGE 11

astart register

request

bexamine

thoroughly

cexamine 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

Page 13: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Extension: Adding perspectives to model based on event log

PAGE 12

astart register

request

bexamine

thoroughly

cexamine 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 14: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Let us play …

Page 15: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Play-Out

PAGE 14

event logprocess model

Page 16: Keynote on Process Mining at SSCI 2010 / CIDM 2011

A

B

C

DE

p2

end

p4

p3p1

start

Play-Out (Classical use of models)

PAGE 15

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

Page 17: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Play-In

PAGE 16

event log process model

Page 18: Keynote on Process Mining at SSCI 2010 / CIDM 2011

A

B

C

DE

p2

end

p4

p3p1

start

Play-In

PAGE 17

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

Page 19: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Replay

PAGE 18

event log process model

• extended model showing times, frequencies, etc.

• diagnostics• predictions• recommendations

Page 20: Keynote on Process Mining at SSCI 2010 / CIDM 2011

A

B

C

DE

p2

end

p4

p3p1

start

Replay

PAGE 19

A B C D

Page 21: Keynote on Process Mining at SSCI 2010 / CIDM 2011

A

B

C

DE

p2

end

p4

p3p1

start

Replay can detect problems

PAGE 20

AC D

Problem!missing token

Problem!token left behind

Page 22: Keynote on Process Mining at SSCI 2010 / CIDM 2011

A

B

C

DE

p2

end

p4

p3p1

start

Replay can extract timing information

PAGE 21

A5B8 C9 D13

5

8

9

13

3

4

5

43

265

8

764

7

74

3

Page 23: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Desire lines in process models

PAGE 22

Page 24: Keynote on Process Mining at SSCI 2010 / CIDM 2011

An example algorithm

PAGE 23

Page 25: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Process Discovery: basic idea

PAGE 24

α

Page 26: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 25

>,→,||,# relations

• Direct succession: x>y iff for some case x is directly followed by y.

• Causality: x→y iff x>y and not y>x.

• Parallel: x||y iff x>y and y>x

• Choice: x#y iff not x>y and not y>x.

a>ba>ca>eb>cb>dc>bc>de>d

a→ba→ca→eb→dc→de→d

b||cc||b

abcdacbdaed

b#ee#bc#ea#d…

Page 27: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 26

Basic Idea Used by α Algorithm (1)

a b

(a) sequence pattern: a→b

Page 28: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 27

Basic Idea Used by α Algorithm (2)

a

b

c

(b) XOR-split pattern:a→b, a→c, and b#c

a

b

c

(c) XOR-join pattern:a→c, b→c, and a#b

a

b

c

(b) XOR-split pattern:a→b, a→c, and b#c

Page 29: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 28

Basic Idea Used by α Algorithm (3)

a

b

c

(d) AND-split pattern:a→b, a→c, and b||c

a

b

c

(e) AND-join pattern:a→c, b→c, and a||b

a

b

c

(d) AND-split pattern:a→b, a→c, and b||c

Page 30: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Example Revisited

PAGE 29

a

b

c

de

p2

end

p4

p3p1

start

Result produced by α algorithm

a>ba>ca>eb>cb>dc>bc>de>d

a→ba→ca→eb→dc→de→d

b||cc||b

b#ee#bc#ea#d…

Page 31: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 30

Page 32: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Challenge: four competing quality criteria

PAGE 31

process discovery

fitness

precisiongeneralization

simplicity

“able to replay event log” “Occam’s razor”

“not overfitting the log” “not underfitting the log”

Page 33: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Flower model

PAGE 32

g

ac

d

ef

b

start end

h

Page 34: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 33

What is the best model?

A D

C

EB

A D

C

EB

ACDACEBCEBCD

990850

Page 35: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 34

What is the best model?

A D

C

EB

A D

C

EB

ACDACEBCEBCD

99888578

Page 36: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 35

What is the best model?

A D

C

EB

A D

C

EB

ACDACEBCEBCD

992853

Page 37: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Example: one log four models

PAGE 36

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”

Page 38: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Model N1

PAGE 37

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 = +

Page 39: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Model N2

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

cexamine casually

dcheckticket

decide reject request

e hend

N2 : fitness = -, precision = +, generalization = -, simplicity = +

Page 40: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Model N3

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

bexamine thoroughly

cexamine casually

d checkticket

decide

pay compensation

reject request

reinitiate requeste

g

hfend

N3 : fitness = +, precision = -, generalization = +, simplicity = +

Page 41: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Model N4

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

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: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Why is process mining such a difficult problem?

• There are no negative examples (i.e., a log shows what has happened but does not show what could not happen).

• Due to concurrency, loops, and choices the search space has a complex structure and the log typically contains only a fraction of all possible behaviors.

• There is no clear relation between the size of a model and its behavior (i.e., a smaller model may generate more or less behavior although classical analysis and evaluation methods typically assume some monotonicity property).

PAGE 41

Page 43: Keynote on Process Mining at SSCI 2010 / CIDM 2011

How can process mining help?

PAGE 42

• Detect bottlenecks• Detect deviations• Performance

measurement• Suggest improvements• Decision support (e.g.,

recommendation and prediction)

• Provide mirror• Highlight important

problems• Avoid ICT failures• Avoid management by

PowerPoint • From “politics” to

“analytics”

Page 44: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 43

Page 45: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Example of a Lasagna process: WMO process of a Dutch municipality

PAGE 44

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.

Page 46: Keynote on Process Mining at SSCI 2010 / CIDM 2011

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 45

Page 47: Keynote on Process Mining at SSCI 2010 / CIDM 2011

C-net discovered using heuristic miner (1/3)

PAGE 46

Page 48: Keynote on Process Mining at SSCI 2010 / CIDM 2011

C-net discovered using heuristic miner (2/3)

PAGE 47

Page 49: Keynote on Process Mining at SSCI 2010 / CIDM 2011

C-net discovered using heuristic miner (3/3)

PAGE 48

Page 50: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Conformance check WMO process (1/3)

PAGE 49

Page 51: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Conformance check WMO process (2/3)

PAGE 50

Page 52: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Conformance check WMO process (3/3)

PAGE 51

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.

Page 53: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Bottleneck analysis WMO process (1/3)

PAGE 52

Page 54: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Bottleneck analysis WMO process (2/3)

PAGE 53

Page 55: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Bottleneck analysis WMO process (3/3)

PAGE 54

flow time of approx. 25 days with a standard deviation of approx. 28

Page 56: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Two additional Lasagna processes

PAGE 55

RWS (“Rijkswaterstaat”)

process

WOZ (“Waardering Onroerende Zaken”)

process

Page 57: Keynote on Process Mining at SSCI 2010 / CIDM 2011

RWS Process

PAGE 56

• 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.

Page 58: Keynote on Process Mining at SSCI 2010 / CIDM 2011

C-net discovered using heuristic miner

PAGE 57

Page 59: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Social network constructed based on handovers of work

PAGE 58

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

Page 60: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Social network consisting of civil servants that executed more than 2000 activities in a 9 month period.

PAGE 59

The darker arcs indicate the strongest relationships in the social network. Nodes having the same color belong to the same clique.

Page 61: Keynote on Process Mining at SSCI 2010 / CIDM 2011

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 60

Page 62: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Discovered process model

PAGE 61

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.

Page 63: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Conformance checker:(fitness is 0.98876214)

PAGE 62

Page 64: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Performance analysis

PAGE 63

bottleneck detection: places are colored based on average durations

information on total flow time

time required to move from one activity to another

Page 65: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Resource-activity matrix(four groups discovered)

PAGE 64

clique 2

clique 1

clique 3

clique 4

Page 66: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 65

Page 67: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Example of a Spaghetti process

PAGE 66

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.).

Page 68: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Fragment18 activities of the 619 activities (2.9%)

PAGE 67

Page 69: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Another example(event log of Dutch housing agency)

PAGE 68

The event log contains 208 cases that generated 5987 events. There are 74 different activities.

Page 70: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 69

Page 71: Keynote on Process Mining at SSCI 2010 / CIDM 2011

PAGE 70

Page 72: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Example of a map

PAGE 71

Road map of The Netherlands. The map abstracts from smaller cities and less significant roads; only the bigger cities, highways, and other important roads are shown. Moreover, cities aggregate local roads and local districts. Also not use of color, size, etc.

Page 73: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Illustrating the problem

PAGE 72

a

cb d

e

start

p1

p2

end

f

g h

i

p7

p8

j

k l

p12

p3

p4

p5

p6

p9

p10

p11

1.0 1.01.0

0.4 0.30.3

1.0 1.0

0.6

0.40.6

0.4

0.40.60.60.40.4

0.3

0.3

x

y z

Page 74: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Classical top level view: low level connections still exist

PAGE 73

p3

p4

p5

p6

p9

p10

p11

x y z

a

cb d

e

start

p1

p2

end

f

g h

i

p7

p8

j

k l

p12

p3

p4

p5

p6

p9

p10

p11

1.0 1.01.0

0.4 0.30.3

1.0 1.0

0.6

0.40.6

0.4

0.40.60.60.40.4

0.3

0.3

x

y z

Page 75: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Seamless zoom

PAGE 74

a

cb d

e

f

g h

i

j

k l

Threshold: 0.3

a

b

e

f

g h

i

j

k l

Threshold: 0.4

a

e

f

h

i

j

k

Threshold: 0.6

a

e

f

i

j

Threshold: 1.0

x

x y z

x y z

x y z

x y z

y z

x y z

x y z

x y z

Page 76: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Example: Reviewing papers(100 cases generating 3730 events)

PAGE 75

WF-net discovered using the α-algorithm

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Fuzzy miner: two views on the same process

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fuzzy model showing all activities

color and width of arc

indicates significance

of connection

fuzzy model showing only two activities

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Balancing between both extremes

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aggregated node containing 10 activities

inner structure of aggregated node

fuzzy model showing all activities

color and width of arc

indicates significance

of connection

fuzzy model showing only two activities

Page 79: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Not a single map!

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Projecting dynamic information on business process maps

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Projecting traffic jams on maps

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Business process movies

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Navigation

• Whereas a TomTom device is continuously showing the expected arrival time, users of today’s information systems are often left clueless about likely outcomes of the cases they are working on.

• Car navigation systems provide directions and guidance without controlling the driver. The driver is still in control, but, given a goal (e.g. to get from A to B as fast as possible), the navigation system recommends the next action to be taken.

• Operational support provides TomTom functionality for business processes.

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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!

Page 85: Keynote on Process Mining at SSCI 2010 / CIDM 2011

Conclusion: two types of processes

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