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Process Mining
Part III – Beyond control-flow mining
Organizational miningDiscovery of social netsExtension algorithms
2
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
3
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
4
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
16
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?
27
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?
37
Bottlenecks – Path Probabilities
What are these path probabilities telling
us?
38
Performance Analysis with Petri Nets