Process Mining: Extension
Mining Algorithms
Ana 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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Types of Algorithms
supports/
controls
“world”
business processes
people
machines
components
organizations
models
analyzes
specifies
configures
implements
(process)
model
analyzes
discovery
conformance
extension
information
system
records
events, e.g.,
messages,
transactions,
etc.
event
logs
Process Mining Tools
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Types of Algorithms
Start
Process Model
Register order
supports/
controls
“world”
business processes
people
machines
components
organizations
models
analyzes
specifies
configures
implements
analyzes
Prepare
shipm ent
information
system
(Re)send bill
Ship goods
records
events, e.g.,
messages,
transactions,
etc.
Contact
custom er
Receive paym ent
Archive order
End
discovery
conformance
extension
Organizational Miner
(process)
Socialmodel
Network
Miner
Process Mining Tools
Analyze Social Network
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Organizational Model
event
logs
Social Network
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Types of Algorithms
Compliance
Process Model
Start
Register order
supports/
controls
“world”
business processes
people
machines
components
organizations
models
analyzes
specifies
configures
implements
analyzes
Prepare
shipm ent
information
system
records
events, e.g.,
messages,
transactions,
etc.
(Re)send bill
Ship goods
Contact
custom er
Receive paym ent
Archive order
End
discovery
conformance
extension
(process)
model
event
logs
Auditing/Security
Process Mining Tools
Conformance Checker
LTL 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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Types of Algorithms
Bottlenecks/
Business Rules
Process Model
Start
Register order
supports/
controls
“world”
business processes
people
machines
components
organizations
models
analyzes
specifies
configures
implements
(process)
model
analyzes
Prepare
shipm ent
information
system
records
events, e.g.,
messages,
transactions,
etc.
(Re)send bill
Ship goods
Contact
custom er
Receive paym ent
Archive order
End
discovery
conformance
extension
Performance Analysis
event
logs
Process Mining 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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Decision Point Analysis: Main Idea
• Detection of data dependencies that affect the
rounting the routing of process instances
Which conditions
influence the choice
between a full check
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
How can
we spot the
in afor
Petri
4. decision
Discoverpoints
the rules
each decision point
5. Return thenet?
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
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Quick Recap Lecture 1: Decision Trees
Attributes
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Classes: Yes/No
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Decision Point Analysis:
Algorithm's Main Steps
1. Read a log + model
elements
areinthe
2. IdentifyWhich
the decision
points
a model
classes and which are
3. Find out which
alternative
branch
has
been
the attributes?
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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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
/faculteit technologie management
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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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WhatTimes
are these average
Bottlenecks – Synchronization
synchronization times
telling us?
1.3 minutes
20.8 minutes
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Bottlenecks – Path Probabilities
What are these path
probabilities telling us?
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Performance Analysis
with Petri Nets
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Process Mining
• Short Recap
• Extension Techniques
– Decision Miner
– Performance Analysis with Petri Nets
• Summary
• Announcements
• Presentation Futura Technology
/faculteit technologie management
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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 plugin 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
/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
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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!
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