Process Mining

Part III – Beyond control-flow mining

Organizational miningDiscovery of social netsExtension algorithms

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OutlinePart I – Introduction to Process Mining

Context, motivation and goal General characteristics of the analyzed processes and logsClassification of Process Mining approaches

Part II – Workflow discoveryInduction of basic Control Flow graphsOther techniques (α-algorithm, Heuristic Miner, Fuzzy mining)

Part III – Beyond control-flow miningOrganizational mining Social net discoveryExtension algorithms

Part IV – Evaluation and validation of discovered modelsConformance CheckLog-based property verification

Part V – Clustering-based Process MiningDiscovery of hierarchical process modelsDiscovery of process taxonomiesOutlier detection

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Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContactcustomer

Archive order

End

ProcessProcess ModelModel

OrganizationalOrganizational ModelModel

SocialSocial NetworkNetwork

Organizational mining Organizational mining techniquestechniques

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Organizational Mining Algorithms

Objective: Discover the organizational model (i.e., roles, departments,etc.) without prior knowledge about the structure of the organizationAid in understanding and improving social and organizational structures

Two types of algorithmsOrganizational Model

Mining of roles and teams in organizationsProM Plug-in: Organizational Miner

Social NetworksDiscovery of relationships among originatorsProM Plug-ins: Social Network Miner and Analyze Social Network

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Organizational Miner

Main idea: Which originators are executing which tasksMethods to mine roles

Default miningDoing Similar Tasks

Methods to mine teamsWorking together

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Organizational Miner

Main idea: Which performers

are executing which tasks

Methods to mine roles Default miningDefault miningDoing Similar Tasks

Methods to mine teamsWorking together

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Organizational Miner

Main idea: Which performers

are executing which tasks

Methods to mine roles Default miningDoing Similar TasksDoing Similar Tasks

Methods to mine teamsWorking together

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Default MiningDefault Mining

Doing Similar TasksDoing Similar Tasks

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Organizational Miner

Main idea: Which performers

are executing which tasks

Methods to mine roles Default miningDoing Similar Tasks

Methods to mine teamsWorking togetherWorking together

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Organizational Miner

Why is the notion of process instances

necessary to mine teams but unnecessary to mine

roles?

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OutlinePart I – Introduction to Process Mining

Context, motivation and goal General characteristics of the analyzed processes and logsClassification of Process Mining approaches

Part II – Workflow discoveryInduction of basic Control Flow graphsOther techniques (α-algorithm, Heuristic Miner, Fuzzy mining)

Part III – Beyond control-flow miningOrganizational mining Social net discoveryExtension algorithms

Part IV – Evaluation and validation of discovered modelsConformance CheckLog-based property verification

Part V – Clustering-based Process MiningDiscovery of hierarchical process modelsDiscovery of process taxonomiesOutlier detection

12

Social Network Miner

Aim: Monitor how individual process instances are routed between originators

MetricsHandover of workSubcontractingReassignmentWorking togetherSimilar task

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Social Network Miner

Aim: Monitor how individual process instances are routed between originators

MetricsHandover of workHandover of workSubcontractingReassignmentWorking togetherSimilar task

JohnJohn MaryMary

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Social Network Miner

Aim: Monitor how individual process instances are routed between originators

MetricsHandover of workSubcontractingSubcontractingReassignmentWorking togetherSimilar task

JohnJohn MaryMary

JohnJohn

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Social Network Miner

Aim: Monitor how individual process instances are routed between originators

MetricsHandover of workSubcontractingReassignmentReassignmentWorking togetherSimilar task

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Social Network Miner

Aim: Monitor how individual process instances are routed between originators

MetricsHandover of workSubcontractingReassignmentWorking togetherSimilar task

Based on ordering Based on ordering relations derived relations derived from a log!from a log!

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Social Network Miner: Example John Alex Lucia Peter Mary John 0 0 0 0 2 Alex 0 0 0 0 0 Lucia 0 0 0 2 2 Peter 0 0 2 0 2 Mary 2 0 2 2 0

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Plugin Analyze Social Network

Better graphical view for the results of the Social Network Miner

Includes different metrics to measure centrality of nodes

Example: subcontracting

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Which testers have never

subcontracted work?

Which testers subcontract the

most?

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OutlinePart I – Introduction to Process Mining

Context, motivation and goal General characteristics of the analyzed processes and logsClassification of Process Mining approaches

Part II – Workflow discoveryInduction of basic Control Flow graphsOther techniques (α-algorithm, Heuristic Miner, Fuzzy mining)

Part III – Beyond control-flow miningOrganizational mining Social net discoveryExtension algorithms

Part IV – Evaluation and validation of discovered modelsConformance CheckLog-based property verification

Part V – Clustering-based Process MiningDiscovery of hierarchical process modelsDiscovery of process taxonomiesOutlier detection in a process mining setting

21

Start

Register order

Prepareshipment

Ship goods

(Re)send bill

Receive paymentContactcustomer

Archive order

End

Bottlenecks/Bottlenecks/Business Business RulesRulesProcessProcess ModelModel

Performance Performance AnalysisAnalysis

Extension techniques

Enhance existing models with information discovered from logsThe Decision Point Analysis plug-in can discover the “business rules” for the moments of choice in a process modelThe Performance Analysis with Petri Nets plug-in provides various KPIs w.r.t. the execution of processes

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Decision Point Analysis: Main IdeaDetection of data dependencies that affect the rounting the routing of process instances

MotivationsMake tacit knowledge explicitBetter understand the process model

WhichWhich conditionsconditions influenceinfluencethe the choicechoice betweenbetween a full a full check and a check and a policypolicy onlyonly oneone??

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Decision Point Analysis: Motivation

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Decision Point Analysis: Approach

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Decision Point Analysis

1. Read a log + model

2. Identify the decision points in a model

3. Find out which alternative branch has been taken for a given process instance and decision point

4. Discover the rules for each decision point

5. Return the enhanced model with the discovered rules

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Decision Point Analysis

1. Read a log + model

2. Identify the decision points in a model

3. Find out which alternative branch has been taken for a given process instance and decision point

4. Discover the rules for each decision point

5. 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 examplesfor decision point "p0"

Discovered decisiontree 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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Extension techniques

Decision Miner

Performance Analysis

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Performance analysis: pattern visualization

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Performance Analysis with Petri Nets

MotivationProvide different Key Performance Indicators (KPIs) relating to the execution of processes

Main ideaReplay the log in a model and detect

BottlenecksThroughput timesExecution timesWaiting timesSynchronization timesPath probabilities etc

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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 Probabilities

What are these path probabilities telling

us?

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Performance Analysis with Petri Nets