process mining: extension mining algorithms

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Process Mining: Extension Process Mining: Extension Mining AlgorithmsMining Algorithms

Ana Karla Alves de MedeirosAna Karla Alves de Medeiros

Eindhoven University of TechnologyDepartment of Information Systems

a.k.medeiros@tue.nl

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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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

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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

Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContactcustomer

Archive order

End

Process ModelProcess Model

Organizational ModelOrganizational Model

Social NetworkSocial Network

Types of Algorithms

Organizational MinerOrganizational Miner

Social Network MinerSocial Network Miner

Analyze Social NetworkAnalyze Social Network

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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

Auditing/SecurityAuditing/Security

Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContactcustomer

Archive order

End

Compliance Compliance Process ModelProcess ModelTypes of Algorithms

Conformance CheckerConformance Checker

LTL CheckerLTL Checker

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Main Points Lecture 4• Organizational mining plug-ins can discover

– Roles/Teams in organizations– Social networks for originators

• Some metrics of social networks are based on ordering relations (e.g., the ordering relations used by the Alpha algorithm)

• Conformance Checker assesses how much a process model matches process instances

• LTL Checker uses logics to verify properties in event logs

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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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

Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContactcustomer

Archive order

End

Bottlenecks/Bottlenecks/Business RulesBusiness RulesProcess ModelProcess Model

Performance AnalysisPerformance Analysis

Types of Algorithms

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Decision Point Analysis: Main Idea

• Detection of data dependencies that affect the rounting the routing of process instances

Which conditions Which conditions influence the choice influence the choice between a full check between a full check and a policy only one?and a policy only one?

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Decision Point Analysis: Motivation

• Make tacit knowledge explicit• Better understand the process model

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Decision Point Analysis: Motivation

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Decision Point Analysis: Algorithm's Main Steps

1. Read a log + model2. Identify the decision points in a model3. Find out which alternative branch has been

taken for a given process instance and decision point

4. Discover the rules for each decision point5. Return the enhanced model with the

discovered rules

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Decision Point Analysis: Algorithm's Main Steps

1. Read a log + model2. Identify the decision points in a model3. Find out which alternative branch has been

taken for a given process instance and decision point

4. Discover the rules for each decision point5. Return the enhanced model with the

discovered rules

How can we spot the decision points in a

Petri net?

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Decision Point Analysis: Algorithm's Main Steps

1. Read a log + model2. Identify the decision points in a model3. Find out which alternative branch has been

taken for a given process instance and decision point

4. Discover the rules for each decision point5. Return the enhanced model with the

discovered rules

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Quick Recap Lecture 1: Decision Trees

AttributesAttributes Classes: Yes/NoClasses: Yes/No

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Decision Point Analysis: Algorithm's Main Steps

1. Read a log + model2. Identify the decision points in a model3. Find out which alternative branch has been

taken for a given process instance and decision point

4. Discover the rules for each decision point5. Return the enhanced model with the

discovered rules

Which elements are the classes and which are

the attributes?

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Step 4

Training examples for decision point "p0"

Discovered decision tree for point "p0"

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Decision Point Analysis: Example in ProM

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Decision Point Analysis: Example in ProM

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Decision Point Analysis

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Performance Analysis with Petri Nets

• Motivation– Provide different Key Performance Indicators (KPIs)

relating to the execution of processes

• Main idea– Replay the log in a model and detect

• Bottlenecks• Throughput times• Execution times• Waiting times• Synchronization times• Path probabilities etc

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Bottlenecks – Waiting Times and Execution Times

How can we spot the difference between waiting and execution

times?

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Bottlenecks – Throughput Times

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Bottlenecks – Synchronization Times

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Bottlenecks – Synchronization Times

20.8 minutes20.8 minutes

1.3 minutes1.3 minutes

What are these average synchronization times

telling us?

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Bottlenecks – Path ProbabilitiesWhat are these path

probabilities telling us?

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Performance Analysiswith Petri Nets

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Summary• Extension techniques enhance existing models

with information discovered from event logs• The Decision Point Analysis plug-in can discover

the “business rules” for the moments of choice in a process model

• The Performance Analysis with Petri Nets plug-in provides various KPIs w.r.t. the execution of processes

• The results of both techniques can be used to create simulation models for CPN Tools

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Process Mining• Short Recap• Extension Techniques

– Decision Miner– Performance Analysis with Petri Nets

• Summary• Announcements• Presentation Futura Technology

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Announcements• Assignment 5

– Individual assignment– Q&A session during Instruction 5– Posting of Report with Answers

• Digital version at StudyWeb (folder Assignment 5)• Printed version to be delivered at secretary’s office of IS

group (room Pav D3) – There will be a box on the desk

• Deadline: March 14th, 2008 at 6pm

• Invited talk after the break!