Open Lecture Wil van der Aalst

Process Mining On the Interplay Between Data

Science and Behavioral Science

Open Lecture High Tech Campus Eindhoven, 11-12-2014

Wil van der Aalst Scientific director of the DSC/e

https://www.coursera.org/course/procmin

A new profession

is emerging, just

like computer

science in the

early 1980-ties!

Requires a combination

of competences!

©Wil van der Aalst & TU/e (use only with permission & acknowledgements)

data mining

processmining visualization

data science

behavioral/social

sciences

domainknowledge

machine learning

large scale distributedcomputing

statistics industrialengineering

databasesstochastics

privacy

algorithms

visual analytics

DSC/e Data Science Competences

data mining

behavioral/social

sciences

domainknowledge

machine learning

industrialengineering

databases

privacy

algorithms

visual analytics

data science

statistics

stochastics

Statistics and Stochastics

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Data Mining and Machine Learning

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Process Mining

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Large Scale Distributed Computing

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Visualization and Visual Analytics

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Domain Knowledge

data mining

data science domain

knowledge

machine learning

behavioral/social

sciences

privacy

algorithms

visual analytics

Behavioral/Social Sciences & Privacy

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Industrial Engineering

data mining

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

Databases & Algorithms

Internet of Events

Internet of Events: 4 sources of event data

Internet of Events

Internet of Content

Internet of Events

“Big Data”

Internet of Content

Internet of People

“social”

Internet of Events

“Big Data”

Internet of Content

Internet of People

“social”

Internet of Things

“cloud”

Internet of Events

“Big Data”

Internet of Content

Internet of People

“social”

Internet of Things

Internet of Places

“cloud” “mobility”

Internet of Events

“Big Data”

"Der Datenflüsterer"

Building a relationship with data

DSC/e: Competences and Research Programs 28 groups involved

Context: Why are we using data science, does it have the intended effect, and will

people accept it?

Analysis: How to turn data into real value (models, answers/decisions, and

visualizations/insights)?

Enabling technologies: How to get the data and deal with computational/

infrastructural challenges (big data and hard questions)?

Probability and Statistics

Stochastic Networks

Data Mining

Process Mining

Visualization

Large-Scale Distributed Systems

Data-Intensive Algorithms

Data-Driven Operations Management

Data-Driven Innovation and Business

Human and Social Analytics

Privacy, Security, Ethics, and Governance

Internet of Things

infrastructurescit

ization

people

[RP1] Process Analytics: Improving Service While Cutting Costs

[RP2] Customer Journey: Correlating Events to Learn and Influence Customer Behavior

[RP3] Smart Maintenance & Diagnostics: Safeguarding Availability

[RP4] Quantified Self: Improving Performance and Well-Being

[RP5] Data Value and Privacy: Economic and Legal Aspects of Data Science

[RP6] Smart Cities: Ensuring Safety and Convenience for Citizens

[RP7] Smart Grids: Data Intensive Infrastructures

[RP1] Process Analytics: Improving Service While Cutting Costs

[RP2] Customer Journey: Correlating Events to Learn and Influence Customer Behavior

[RP3] Smart Maintenance & Diagnostics: Safeguarding Availability

[RP4] Quantified Self: Improving Performance and Well-Being

[RP5] Data Value and Privacy: Economic and Legal Aspects of Data Science

[RP6] Smart Cities: Ensuring Safety and Convenience for Citizens

[RP7] Smart Grids: Data Intensive Infrastructures

Data Science Flagship (Philips & DSC/e)

• 4 Strategic topics

• 4 TU/e departments

• 16 PhD students

• 30 Data science specialists

1. Data Driven Value Propositions

2. Healthcare Smart Maintenance

3. Optimizing Healthcare Workflows

4. Continuous Personal Health

Process Mining Let's Play

data mining

processmining

visualization

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

data mining

processmining

visualization

data science

behavioral/social

sciences

domainknowledge

machine learning

privacy

algorithms

visual analytics

formal methods

business process

management

concurrency

business process re-engineering

process science

modelchecking Petri nets

register travel

request (a)

get detailed motivation letter (c)

get support from local

manager (b)

check budget by finance (d)

decide (e)

accept request (g)

reject request (h)

reinitiate request (f)

start end

Case Activity Timestamp Resource

432 register travel request (a) 18-3-2014:9.15 John

432 get support from local manager (b) 18-3-2014:9.25 Mary

432 check budget by finance (d) 19-3-2014:8.55 John

432 decide (e) 19-3-2014:9.36 Sue

432 accept request (g) 19-3-2014:9.48 Mary

Play-In

Play-Out

Replay

Let's play

Play-Out

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

432 decide (e) 19-3-2014:9.36 Sue

Play Out: A possible scenario

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

a b d e g

432 decide (e) 19-3-2014:9.36 Sue

Play Out: Another scenario

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

a c d e h f b d e

Play Out: Process model allows for many

more scenarios

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

abdeg abcefbdeh

adceg adbeh

acbefbdeh acdefcdefbdeh

adcefcdefbdefbdeg abdeg adceh adbeh

acbefbdeg acdefcdefbdeh adcefcdefbdefbdeg

Play-In

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

432 decide (e) 19-3-2014:9.36 Sue

Loesje van

der Aalst

desire line

Play In: Simple process allowing for 4 traces

abdeg adbeg

abdeh adbeh abdeh abdeh

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

Play In: Process allowing for more traces

abdeg abcefbdeh

adceg adbeh

acbefbdeh acdefcdefbdeh adcefcdefbdefbdeg abdeg adceh adbeh

acbefbdeg acdefcdefbdeh adcefcdefbdefbdeg

abdeg adceh

adbeh acbefbdeg

acdefcdefbdeh

adcefcdefbdefbdeg abdeg

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

No modeling needed!

Example Process Discovery (Dutch housing agency, 208 cases, 5987 events)

Replay register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

432 decide (e) 19-3-2014:9.36 Sue

event data process

desire line

very safe

system

Replay

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

a c d e g

Replay

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

a c check budget (d)

is missing!

Replay

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

a c d e g h reject request (h) is

impossible ?

Conformance Checking (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)

Replay with timestamps

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

a9.15 c9.20 d9.35 e10.15 g11.30

Replay with timestamps

register travel

request (a)

manager (b)

decide (e)

accept request (g)

reject request (h)

start end

Performance Analysis Using Replay (WOZ objections Dutch municipality, 745 objections, 9583 event, f= 0.988)

software

system

(process)

models

analyzes

discovery

records

events, e.g.,

messages,

transactions,

specifies

configures

implements

analyzes

supports/

controls

enhancement

conformance

“world”

people machines

organizations

components

business

processes

Overview

Play-In

Play-Out

Replay

Process Mining Software

600+ plug-ins available covering the

whole process mining spectrum

©Wil van der Aalst & TU/e (use only with permission & acknowledgements) ©Wil van der Aalst & TU/e (use only with permission & acknowledgements)

Process Discovery

Starting point for process mining:

Event data

student name course name exam date mark

Peter Jones Business Information systems 16-1-2014 8

Sandy Scott Business Information systems 16-1-2014 5

Bridget White Business Information systems 16-1-2014 9

John Anderson Business Information systems 16-1-2014 8

Sandy Scott BPM Systems 17-1-2014 7

Bridget White BPM Systems 17-1-2014 8

Sandy Scott Process Mining 20-1-2014 5

Bridget White Process Mining 20-1-2014 9

John Anderson Process Mining 20-1-2014 8

… … … …

case id activity name timestamp other data

every row is an event

(here: an exam attempt)

Another event log: patient treatment

patient activity timestamp doctor age cost

5781 make X-ray 23-1-2014@10.30 Dr. Jones 45 70.00

5541 blood test 23-1-2014@10.18 Dr. Scott 61 40.00

5781 CT scan 23-1-2014@11.10 Dr. Fox 45 1200.00

5833 surgery 23-1-2014@12.34 Dr. Scott 24 2300.00

5781 handle payment 23-1-2014@12.41 Carol Hope 45 0.00

5541 radiation therapy 23-1-2014@13.57 Dr. Jones 61 140.00

5541 radiation therapy 23-1-2014@13.08 Dr. Jones 61 140.00

… … … … … …

case id activity name timestamp other data resource

Another event log: order handling

number

activity timestamp user product quantity

9901 register order 22-1-2014@09.15 Sara Jones iPhone5S 1

9901 check stock 22-1-2014@09.49 Pete Scott iPhone5S 1

9901 ship order 22-1-2014@10.11 Sue Fox iPhone5S 1

9901 handle payment 22-1-2014@10.41 Carol Hope iPhone5S 1

9902 cancel order 22-1-2014@11.08 Carol Hope iPhone5S 2

… … … … … …

case id activity name timestamp other data resource

Simplifying event logs when focusing on

control-flow

number

activity timestamp user product quantity

9901 ship order 22-1-2014@10.11 Sue Fox iPhone5S 1

9901 handle payment 22-1-2014@10.41 Carol Hope iPhone5S 1

9902 cancel order 22-1-2014@11.08 Carol Hope iPhone5S 2

… … … … … … [ register_order, check_stock,ship_order,handle_payment,

register_order, check_stock,cancel_order,

register_order, check_stock , …]

Simple event log

• An event log is a multiset of traces (same trace

may appear multiple times).

• A trace is a sequence of activity names (we

abstract from all other attributes, but events are

ordered).

mining

Goal of Alpha algorithm

Event log contains all possible

traces of model and vice versa.

Another example

Generalization: event

log contains only

subset of all possible

traces of model.

Notation is less relevant (e.g. BPMN)

start end

Another BPMN example

start end

>,,||,# relations

• Direct succession: x>y iff

for some case x is directly

followed by y.

• Causality: xy iff x>y and

not y>x.

• Parallel: x||y iff x>y and

• Choice: x#y iff not x>y and

not y>x.

ed b||c

Basic Idea Used by Alpha Algorithm (1)

(a) sequence pattern: a→b

(b) XOR-split pattern:

a→b, a→c, and b#c

(b) XOR-split pattern:

a→b, a→c, and b#c

(c) XOR-join pattern:

b→d, c→d, and b#c

(d) AND-split pattern:

a→b, a→c, and b||c

(e) AND-join pattern:

b→d, c→d, and b||c

(d) AND-split pattern:

a→b, a→c, and b||c

Example Revisited

Result produced by

the Alpha algorithm

inductive

mining

Split event logs based on activity labels

Split {a,b,c,d,e,f,g,h} into {a,b,c,d} and {e,f,g}

using sequence decomposition

Result

Split {a,b,c,d} into {a} and {b,c,d} using

sequence decomposition

Result

Split {e,f,g} into {e} and {f,g} using sequence

decomposition

Result

seq seq

Split {f,g} into {f} and {g} using XOR

decomposition

seq seq

Result

seq seq

Split {b,c,d} into {b,c} and {d} using AND

decomposition

seq seq

Result

seq seq

Split {b,c} into {b} and {c} using XOR

decomposition

seq seq

Result

seq seq par

xor …

no further decomposition is possible

Process tree

abdefacdefadbefadcefabdegacdegadbegadceg

abdacdadbadc

bdcddbdc

astart

gstart end

Conclusion

data science process science

process mining

data-oriented analysis

process model analysis (simulation, verification,

optimization, gaming, etc.)

(data mining, machine learning,

business intelligence)

process mining

data-oriented analysis

process model analysis

performance-oriented

questions, problems and

solutions

compliance-oriented

questions, problems and

solutions

(simulation, verification,

optimization, gaming, etc.)

(data mining, machine learning,

business intelligence)

Process Mining

Data Science in Action

38.000+ people joined!

©Wil van der Aalst & TU/e (use only with permission & acknowledgements) ©Wil van der Aalst & TU/e (use only with permission & acknowledgements)

Open Lecture Wil van der Aalst

Technology

Transcript of Open Lecture Wil van der Aalst

HIERARCHICAL CONFORMANCE CHECKING OF PROCESS MODELS BASED ON EVENT LOGS Jorge Munoz-Gama, Josep Carmona and Wil M.P. van der Aalst.

History-Dependent Petri Nets Kees van Hee, Alexander Serebrenik, Natalia Sidorova, Wil van der Aalst ?

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Process Mining for Ubiquitous Mobile Systems An Overview and a Concrete Algorithm Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department.

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