Process MiningProcess Miningg: Controlg: Control-Flow ... - ut

Process MiningProcess MiningProcess MiningProcess MiningMining AlMining Algg

L t ML t MLecturer: MLecturer: M

(using slides by Ana (using slides by Ana Karla Karla University of TechnologyUniversity of Technology

g: Controlg: Control--FlowFlowg: Controlg: Control Flow Flow lgorithmslgorithmsgg

M l DM l DMarlon DumasMarlon Dumas

Alves de Alves de Medeiros, Eindhoven Medeiros, Eindhoven y y –– www.processmining.org)www.processmining.org)

1

Types of Process Minin Start

Register order

Process ModelProcess Modelng

Prepareshipment

(Re)send bill

Ship goods

Receive paymentContactcustomer

Archive order

End

Organizational ModelOrganizational Model

Social NetworkSocial Network

2

Types of Mining Algorith Start

Register order

Compliance Compliance Process ModelProcess Model

hms

Prepareshipment

(Re)send bill

Ship goods


Archive order

End

Auditing/SecurityAuditing/Security

3

Types of Mining Algorith Start

Register order

Bottlenecks/Bottlenecks/Business RulesBusiness RulesProcess ModelProcess Model

hms

Prepareshipment

(Re)send bill

Ship goods


Archive order

End

Performance AnalysisPerformance Analysis

4

Control-Flow Mining

1. Start2 Get Ready1. Start2. Get Ready3. Travel by Train4. Beta Event Starts5. Visit Brewery6. Have Dinner7 Go Home

2. Get Ready3. Travel by Train4. Beta Event Starts5. Give a Talk6 Visit Brewery

1. Start2. Get Ready3. Travel by Car4. Beta Event Starts5. Give a Talk

1. Start2. Get Ready3. Travel by Car4. Conference Starts7. Go Home

8. Travel by Train

6. Visit Brewery7. Have Dinner8. Go Home9. Travel by Train

6. Visit Brewery7. Have Dinner8. Go Home9. Pay Parking10. Travel by Car

4. Conference Starts5. Join Reception6. Have Dinner7. Go Home8. Pay Parking9 Travel by Car

EventEvent

10. Travel by Car9. Travel by Car10. End

EventEventLogLog

Discovery TechniquesDiscovery TechniquesControlControl--Flow MiningFlow Mining

StartStartStart

Get Ready

Travel by CarTravel by Train

Get ReadyGet Ready

Travel byTravel byTrainTrain

Travel byTravel byCarCar

MinedMined

BETA PhD Day Starts

Give a Talk

Conference StartsConference Starts

Give a TalkGive a Talk MinedMinedModelModelVisit Brewery

H Di

Join ReceptionJoin Reception

Have DinnerHave Dinner Have Dinner

Go Home

Have DinnerHave Dinner

Go HomeGo Home

Travel by Train Pay for Parking

Travel by Car


Travel byTravel by

Pay Pay ParkingParking

s: s: gg

End

CarCar

EndEnd

5

Mining Common Construct

• Sequence• Splits• Splits• Joins• Loops• Non Free Choice• Non-Free Choice• Invisible Tasks• Duplicate Tasks

tsGet Ready

Get Get ReadyReady


Defence Starts



Defense StartsDefense Starts

Ask Question Give a Talk


Ask QuestionAsk Question

Defence Ends

Give a TalkGive a Talk

Defense EndsDefense Ends

Have Drinks


Have DrinksHave Drinks

Pay Pay

Go HomeGo HomeGo Home

Travel byTravel by yyParkingParking


Travel by Car

yyTrainTrain


6

CarCar

Mining Common Construct

• Sequence• Splits• Splits• Joins• Loops• Non Free Choice• Non-Free Choice• Invisible Tasks• Duplicate Tasks

+ noise in logs!

tsGet Ready

Get Get ReadyReady


Defence Starts




Ask Question Give a Talk


Ask QuestionAsk Question

Defence Ends



Have Drinks


Have DrinksHave Drinks

Pay Pay


Travel byTravel by yyParkingParking


Travel by Car

yyTrainTrain


7

CarCar

Process Mining Algorithms

• α-algorithm• Heuristics Miner• Heuristics Miner• Genetic Miner• Fuzzy Miner

s

8

α-algorithm

1. Read a log2 Get the set of tasks2. Get the set of tasks3. Infer the ordering relat4. Build the net based on5 Output the net5. Output the net

tions Core Step!Core Step!n inferred relations

9

α-algorithm - Ordering Rela

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

• Causality: x→y iff x>y and not y>xx>y and not y>x.

• Parallel: x||y iff x>y dand y>x

• Unrelated: x#y iff ynot x>y and not y>x.

ations >,→,||,#

case 1 : task A case 1 : task A case 2 : task A case 2 : task A case 3 : task Acase 3 : task A ABCDABCDcase 3 : task A case 3 : task A case 3 : task B case 3 : task B case 1 : task B case 1 : task B case 1 : task Ccase 1 : task C

ABCDABCDACBDACBD

case 1 : task C case 1 : task C case 2 : task C case 2 : task C case 4 : task A case 4 : task A case 2 : task B case 2 : task B

EFEF

......

A>BA>BA>BA>BA>CA>CB>CB>CB>DB>D

AA→→BBAA→→CCB||CB||C

C||BC||BB>DB>DC>BC>BC>DC>DE FE F

AA→→CCBB→→DDCC→→DD

C||BC||B

10E>FE>F EE→→FF

AA→→BB

ABCDABCDACBDACBD

AA→→CCBB→→DDCC→→DD

B||CB||CC||BC||B

EFEFCC→→DDEE→→FF

A

α-algorithm - Insight

B

C

D

C

E F

11

α-algorithm – Log prope

• If log is complete with rebe used to mine SWF-nbe used to mine SWF-n

• Structured Workflow Neimplicit places and the foimplicit places and the focannot be used:

erties + target nets

espect to relation >, it can et without short loopset without short loops

ets (SWF-nets) have no ollowing two constructsollowing two constructs

12

α-algorithm – No short lB>B and not B>B implies B→B (im

A>B and B>A implies A||B and B

loops Why no short

mpossible!)short

loops?

One-length

Two-length

||A instead of A→B and B→A

13

α-algorithm – Common

Why no d li tduplicate

tasks?

• No invisible tasks, non-free• No noisy logsy g

Constructs

Why notWhy not invible tasks?tasks?

Why noise-free logs?

e-choice or duplicate tasks

14


• α-algorithm• Heuristics Miner• Heuristics Miner• Genetic Algorithm• Fuzzy Miner

s

15

Heuristics Miner

1. Read a log2 Get the set of tasks2. Get the set of tasks3. Infer the ordering relat

frequencies4 Build the net based on4. Build the net based on5. Output the net

tions based on their

n inferred relationsn inferred relations

16

Heuristics Miner

• The more frequently a taanother task B, and theanother task B, and the opposite occurs, the higcausally follows B!y

• Robust to invisible tas• No non-free-choice or• No non-free-choice or

ask A directly follows less frequently theless frequently the

gher the probability that A

sks and noisy logsr duplicate tasks

17r duplicate tasks



s

18

Genetic Process Mining (G

• Genetic Algorithms + Pr• Genetic Algorithms• Genetic Algorithms

– Search technique that mimi bi l i l tin biological systems

• Advantagesg– Tackle all common structu– Robust to noiseRobust to noise

• Disadvantages– Computational Time

GPM)

rocess Mining

mics the process of evolution

ural constructs

19

Genetic Process Mining

Algorithm:

Internal RepresentationFitness Measure

Genetic Operators

g (GPM)

20

GPM – Fitness Measure

TraveTrave

• Guides the search! TT

ConferenConferen

GG

the search!

GG

VisVis

HHHH

TraveTraveTT

StartStartStart

Get Ready


Get ReadyGet Ready

el byel by Travel byTravel by

BETA PhD Day Starts

Give a Talk

TrainTrain CarCarnce Startsnce Starts


Visit Brewery


sit Brewerysit Brewery


Go Home


Go HomeGo Home


Travel by Car

el byel byTrainTrain



End

CarCar

EndEnd

21


Start

Get Ready


BETA PhD Day Starts

Visit Brewery

Give a Talk

y

Have Dinner

Go Home


Travel by Car

End

22

GPM – Fitness MeasureSS

BETA PhD Day St

SS

Conference StartsConference StartsOvergeneral

solution

Start

Get ReadyGive a Talk



BETA PhD Day Starts

Visit Brewery

Give a Talk

Visit Brewery

Visit BreweryVisit Brewery

y

Have Dinner

Go Home


Visit Brewery

Travel by Car

End

Travel by Car


Punish for the amount of enabled tasks during the parsing!

St tSt t

Start

Travel by Traintarts

StartStart Travel byTravel byTrainTrain

Get Ready

Get ReadyGet Ready

Have Dinner


Pay for Parking

Go Home

End Go HomeGo Home


EndEnd

23


Travel byTravel by

Overspecific solution

Start

Get Ready

Travel byTravel by


BETA PhD Day Starts

Visit Brewery

Give a Talk

Start

Get Ready

Travel by TrainBETA PhD Day Starts

Have Dinner

Give a Talk

y

Have Dinner

Go Home


Visit Brewery

Go Home

Pay for Parking

Travel by Car

End

Travel by Car

End Travel byTravel byPunish for the amount of duplicate tasks with common input/output tasks!

StartStartStart Start StartStart

Get Ready


Get ReadyGet Ready Get Ready

Travel by Train Travel by Car

Get ReadyGet Ready

Travel by CarTravel by Cary Trainy Train

BETA PhD Day StartsBETA PhD Day StartsBETA PhD Day Starts BETA PhD Day Starts

Travel by CarTravel by Car

Conference StartsConference Starts

y Trainy Train

Visit Brewery


Visit BreweryVisit Brewery Visit Brewery


Visit BreweryVisit Brewery

Have Dinner

Go Home




Go HomeGo Home

Travel by Train Pay for ParkingTravel by Train Pay for Parkingy Trainy Train Pay ParkingPay Parking

Travel by Car

End

Travel by Car

EndEnd End

Travel by CarTravel by Car

EndEnd

24



s

25

Fuzzy Miner - Motivation

Mine less structured proocesses!26

Fuzzy Miner - Motivation

27

Fuzzy Miner

28

No “Ask

Fuzzy MinerQuestion” or “Give Talk”!

Abstractingmore from d

All details!

g even details!

29

Complementary Technique

• Mining Roles, Teams, S• Decision Mining• Decision Mining

– Using classification techn• Performance Analysis• And for more please co• And for more, please co

– Estonian Summer SchoolScience http://courses csScience http://courses.cs

– Lectures by Wil van der A

es

Social Networks

iques

onsider attending:onsider attending:l in Computer and Systems ut ee/schools/esscass2009/.ut.ee/schools/esscass2009/

Aalst on Process Mining

30

Documents

Process MiningProcess Miningg: Controlg: Control-Flow ... - ut