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Presentation transcript:

/faculteit technologie management 1 Process Mining: Extension Mining Algorithms Ana Karla Alves de Medeiros Ana 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 Types of Algorithms

/faculteit technologie management 5 Process Model Organizational Model Social Network Types of Algorithms Organizational Miner Social Network Miner Analyze Social Network

/faculteit technologie management 6 Auditing/Security Compliance Process Model Types of Algorithms Conformance Checker LTL 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 Bottlenecks/ Business Rules Process Model Performance 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 influence the choice between a full check 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 16 Decision Point Analysis: Approach

/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 Attributes Classes: 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 26 Decision Point Analysis - Challenges Noise –Need for robust extraction and data mining techniques Some control-flow constructs may be "misleading" –Invisible tasks –Duplicate Tasks –Loops

/faculteit technologie management 27 Decision Point Analysis - Challenges Noise –Need for robust extraction and data mining techniques Some control-flow constructs may be "misleading" –Invisible tasks –Duplicate Tasks –Loops

/faculteit technologie management 28 Decision Point Analysis - Challenges Noise –Need for robust extraction and data mining techniques Some control-flow constructs may be "misleading" –Invisible tasks –Duplicate Tasks –Loops

/faculteit technologie management 29 Decision Point Analysis - Challenges Noise –Need for robust extraction and data mining techniques Some control-flow constructs may be "misleading" –Invisible tasks –Duplicate Tasks –Loops

/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 minutes 1.3 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 Analysis with 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 14 th, 2008 at 6pm Invited talk after the break!