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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 Technology
Department of Information Systems
[email protected]
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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 paymentContact
customer
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 paymentContact
customer
Archive order
End
Compliance Compliance Process ModelProcess Model
Types 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 paymentContact
customer
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 + 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: Algorithm's Main Steps
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
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 + 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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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 + 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 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 Probabilities
What 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!