faculteit technologie management 1 process mining: extension mining algorithms ana karla alves de...
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/faculteit technologie management 1
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
/faculteit technologie management 2
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 3
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 4
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
/faculteit technologie management 5
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
/faculteit technologie management 6
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
/faculteit technologie management 7
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
/faculteit technologie management 8
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 9
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 10
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
/faculteit technologie management 11
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 12
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 13
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?
/faculteit technologie management 14
Decision Point Analysis: Motivation
• Make tacit knowledge explicit• Better understand the process model
/faculteit technologie management 15
Decision Point Analysis: Motivation
/faculteit technologie management 17
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
/faculteit technologie management 18
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?
/faculteit technologie management 19
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
/faculteit technologie management 20
Quick Recap Lecture 1: Decision Trees
AttributesAttributes Classes: Yes/NoClasses: Yes/No
/faculteit technologie management 21
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?
/faculteit technologie management 22
Step 4
Training examples for decision point "p0"
Discovered decision tree for point "p0"
/faculteit technologie management 23
Decision Point Analysis: Example in ProM
/faculteit technologie management 24
Decision Point Analysis: Example in ProM
/faculteit technologie management 25
Decision Point Analysis
/faculteit technologie management 30
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 31
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 32
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
/faculteit technologie management 33
Bottlenecks – Waiting Times and Execution Times
How can we spot the difference between waiting and execution
times?
/faculteit technologie management 34
Bottlenecks – Throughput Times
/faculteit technologie management 35
Bottlenecks – Synchronization Times
/faculteit technologie management 36
Bottlenecks – Synchronization Times
20.8 minutes20.8 minutes
1.3 minutes1.3 minutes
What are these average synchronization times
telling us?
/faculteit technologie management 37
Bottlenecks – Path Probabilities
What are these path probabilities telling us?
/faculteit technologie management 38
Performance Analysiswith Petri Nets
/faculteit technologie management 39
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 40
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 41
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
/faculteit technologie management 42
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 43
Process Mining
• Short Recap• Extension Techniques
– Decision Miner– Performance Analysis with Petri Nets
• Summary• Announcements• Presentation Futura Technology
/faculteit technologie management 44
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!