faculteit technologie management 1 Process Mining: Control-Flow Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University

/faculteit technologie management 1

Process Mining: Control-Flow Process Mining: Control-Flow Mining AlgorithmsMining Algorithms

Ana Karla Alves de MedeirosAna Karla Alves de Medeiros

Eindhoven University of Technology

Department of Information Systems

[email protected]


Process Mining

• Short Recap• Types of Process Mining Algorithms• Common Constructs• Input Format• α-algorithm• Heuristics Miner• Genetic Miner• Fuzzy Miner


Process Mining



Process Mining

1. Register at hospital

2. Talk to doctor

3. Take blood test

4. Take X-ray

5. Get results

6. Talk to doctor

7. Go home


2. Talk to doctor

3. Undergo surgery

4. Take medicines

5. Take blood sample

6. Talk to doctor

7. Go home


2. Talk to doctor

3. Undergo surgery

4. Take medicines


6. Talk to doctor

7. Go home

EventEventLogsLogs


2. Talk to doctor

3. Undergo surgery

4. Take medicines


6. Talk to doctor

7. Go home


2. Talk to doctor

3. Undergo surgery

4. Take medicines


6. Talk to doctor

7. Go home


2. Talk to doctor

3. Undergo surgery

4. Take medicines


6. Talk to doctor

7. Go home

Ana Karla A. de Medeiros

Process Mining is about retrieving information about event logs


Process Mining Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContact

customer

Archive order

End

Performance AnalysisPerformance Analysis

Process ModelProcess Model Organizational ModelOrganizational Model

Social NetworkSocial Network

EventEventLogLog

MiningMiningTechniquesTechniques

Auditing/SecurityAuditing/Security

MinedMinedModelsModels

Ana Karla A. de Medeiros

Process Mining is about retrieving information about event logs


Event Logs are Everywhere!aaaaaaaaaaaaaaaaaaaaaaaa

aaaaaaaaaaaaaaaaaaaaaaaa

Start

Get Ready

Travel by CarTravel by Train

BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home

Travel by Train Pay for Parking

Travel by Car

End

Give a Talk

Machines, Municipalities, Airports,Machines, Municipalities, Airports,Internet, Hospitals, etc.Internet, Hospitals, etc.


Tools

• www.processmining.org• ProM 4.2• ProMimport• Free tools!


Process Mining



Process Mining



information system

modelsanalyzes

discovery

records events, e.g., messages,

transactions, etc.

specifies configures

implements

analyzes

supports/controls

extensionconformance

“world”people machines

organizationscomponents

business processes

(process)model

event logs

Process Mining Tools

Types of Algorithms


information system

modelsanalyzes

discovery


transactions, etc.


implements

analyzes

supports/controls




business processes

(process)model

event logs


Start

Register order

Prepareshipment

Ship goods

(Re)send bill


customer

Archive order

End

Process ModelProcess Model

Organizational ModelOrganizational Model

Social NetworkSocial Network

Types of Algorithms


information system

modelsanalyzes

discovery


transactions, etc.


implements

analyzes

supports/controls




business processes

(process)model

event logs


Auditing/SecurityAuditing/Security

Start

Register order

Prepareshipment

Ship goods

(Re)send bill


customer

Archive order

End

Compliance Compliance Process ModelProcess Model

Types of Algorithms


information system

modelsanalyzes

discovery


transactions, etc.


implements

analyzes

supports/controls




business processes

(process)model

event logs


Start

Register order

Prepareshipment

Ship goods

(Re)send bill


customer

Archive order

End

Bottlenecks/Bottlenecks/Business RulesBusiness RulesProcess ModelProcess Model

Performance AnalysisPerformance Analysis

Types of Algorithms


Process Mining

EventEventLogLog

Discovery Techniques: Discovery Techniques: Control-Flow MiningControl-Flow Mining

MinedMinedModelModel

1. Start

2. Get Ready

3. Travel by Train

4. Beta Event Starts

5. Visit Brewery

6. Have Dinner

7. Go Home

8. Travel by Train

1. Start

2. Get Ready

3. Travel by Train


5. Give a Talk

6. Visit Brewery

7. Have Dinner

8. Go Home

9. Travel by Train

1. Start

2. Get Ready

3. Travel by Car


5. Give a Talk

6. Visit Brewery

7. Have Dinner

8. Go Home

9. Pay Parking

10. Travel by Car

1. Start

2. Get Ready

3. Travel by Car

4. Conference Starts

5. Join Reception

6. Have Dinner

7. Go Home

8. Pay Parking

9. Travel by Car

10. End

Start

Get Ready


BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home


Travel by Car

End

Give a Talk

StartStart

Get ReadyGet Ready

Travel byTravel by TrainTrain

Travel byTravel by CarCar

Conference StartsConference Starts

Give a TalkGive a Talk

Join ReceptionJoin Reception

Have DinnerHave Dinner

Go HomeGo Home



Pay Pay ParkingParking

EndEnd


Process Mining



Process Mining



Common Constructs

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


Get Ready


Defence Starts

Ask Question

Defence Ends

Go Home


Travel by Car

Give a Talk

Have Drinks

Get Get ReadyReady



Defense StartsDefense Starts


Ask QuestionAsk Question

Defense EndsDefense Ends

Go HomeGo Home



Have DrinksHave Drinks


Common Constructs

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


Get Ready


Defence Starts

Ask Question

Defence Ends

Go Home


Travel by Car

Give a Talk

Have Drinks

Get Get ReadyReady



Defense StartsDefense Starts


Ask QuestionAsk Question

Defense EndsDefense Ends

Go HomeGo Home



Have DrinksHave Drinks

+ noise in logs!


Process Mining



Process Mining



Event Log: Mining XML (MXML)

The notion The notion of which of which tasks tasks belong to a belong to a same same instance is instance is crucial for crucial for applying applying process process mining mining techniques!techniques!





Compulsory Compulsory fields!fields!


Process Mining



Process Mining



α-algorithm

1. Read a log

2. Get the set of tasks

3. Infer the ordering relations

4. Build the net based on inferred relations

5. Output the net

Core Step!Core Step!


• 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 y>x

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

case 1 : task A case 1 : task A case 2 : task A case 2 : task A case 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 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 ......

A>BA>BA>CA>CB>CB>CB>DB>DC>BC>BC>DC>DE>FE>F

AABB

AACC

BBDD

CCDD

EEFF

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

ABCDABCD

ACBDACBD

EFEF

α-algorithm - Ordering Relations >,,||,#


Let W be a workflow log over T. (W) is defined as follows.

1. TW = { t T W t },

2. TI = { t T W t = first() },

3. TO = { t T W t = last() },

4. XW = { (A,B) A TW B TW a Ab B a W b a1,a2 A a1#W

a2 b1,b2 B b1#W b2 },

5. YW = { (A,B) X (A,B) XA A B B (A,B) = (A,B) },

6. PW = { p(A,B) (A,B) YW } {iW,oW},

7. FW = { (a,p(A,B)) (A,B) YW a A } { (p(A,B),b) (A,B) YW b

B } { (iW,t) t TI} { (t,oW) t TO}, and

8. (W) = (PW,TW,FW).

α-algorithm - Formalization


A

B

C

D

E F

ABCDABCD

ACBDACBD

EFEF

AABB

AACC

BBDD

CCDD

EEFF

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

α-algorithm - Insight


• If log is complete with respect to relation >, it can be used to mine SWF-net without short loops

• Structured Workflow Nets (SWF-nets) have no implicit places and the following two constructs cannot be used:

α-algorithm – Log properties + target nets


One-length

Two-length

B>B and not B>B implies BB (impossible!)

A>B and B>A implies A||B and B||A instead of AB and BA

α-algorithm – No short loops Why no short

loops?


• No invisible tasks, non-free-choice or duplicate tasks• No noisy logs

α-algorithm – Common Constructs

Why no duplicate

tasks?

Why not invible tasks?

Why noise-free

logs?


Process Mining

• Short Recap• Types of Process Mining Algorithms• Common Constructs• Input Format• α-algorithm• Heuristics Miner• Genetic Algorithm• Fuzzy Miner


Process Mining

• Short Recap• Types of Process Mining Algorithms• Common Constructs• Input Format• α-algorithm• Heuristics Miner• Genetic Algorithm• Fuzzy Miner


Heuristics Miner

1. Read a log

2. Get the set of tasks

3. Infer the ordering relations based on their frequencies

4. Build the net based on inferred relations

5. Output the net


Heuristics Miner

Insight:The more frequently a task A directly follows another task B, and the less frequently the opposite occurs, the higher the probability that A causally follows B!


• No non-free-choice or duplicate tasks• Robust to invisible tasks and noisy logs

α-algorithm – Common Constructs

Why no non-free-choice?

Why no guarantee whole net will be correct?


Process Mining



Process Mining



Genetic Process Mining (GPM)

• Genetic Algorithms + Process Mining• Genetic Algorithms

– Search technique that mimics the process of evolution in biological systems

• Advantages– Tackle all common structural constructs– Robust to noise

• Disadvantages– Computational Time


Genetic Process Mining (GPM)

Algorithm:

Internal Representation

Fitness Measure

Genetic Operators


GPM – Fitness MeasureStart

Get Ready


BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home


Travel by Car

End

Give a Talk

StartStart

Get ReadyGet Ready





Visit BreweryVisit Brewery


Go HomeGo Home




EndEnd

• Guides

the search!


GPM – Fitness Measure

Start

Get Ready


BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home


Travel by Car

End

Give a Talk



Start

Get Ready


BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home


Travel by Car

End

Give a Talk

Start

Get Ready

Travel by TrainBETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home

Pay for Parking

Travel by Car

End

Give a Talk

StartStart

Get ReadyGet Ready






Go HomeGo Home


EndEnd


Punish for the amount of enabled tasks during the parsing!

Overgeneral solution



Start

Get Ready


BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home


Travel by Car

End

Give a Talk

Start

Get Ready

Travel by TrainBETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home

Pay for Parking

Travel by Car

End

Give a Talk

Start

Get Ready


BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home


Travel by Car

End

Give a Talk

StartStart Start

Get ReadyGet Ready Get Ready

Travel by Train Travel by Car

BETA PhD Day StartsBETA PhD Day Starts BETA PhD Day Starts

Give a Talk

Visit BreweryVisit Brewery Visit Brewery

Have DinnerHave Dinner Have Dinner

Go HomeGo HomeGo Home


Travel by Car

EndEnd End

StartStart

Get ReadyGet Ready

Travel by CarTravel by Car





Go HomeGo Home

Travel by TrainTravel by Train

Travel by CarTravel by Car

Pay ParkingPay Parking

EndEnd

Travel by TrainTravel by Train

Punish for the amount of duplicate tasks with common input/output tasks!

Overspecific solution


GPM – DGA ProM Plug-in

Why does the GA Miner takes so

much time?

How could we improve its running time without

changing the code?


Process Mining



Process Mining



Fuzzy Miner - Motivation

Mine less structured processes!


Fuzzy Miner - Motivation


Fuzzy Miner


Fuzzy Miner

No “Ask Question” or “Give Talk”!

All details!

Abstracting even more from details!


Conclusions

• The notion of a process instance is crucial!• Ordering of tasks is the basic information• Frequencies are important to handle noise• Local approaches

– α-algorithm, Heuristics Miner

• Global approaches– Genetic Miner and Fuzzy Miner

Documents

faculteit technologie management 1 Process Mining: Control-Flow Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University