Process Mining Control flow process discovery Fabrizio Maria Maggi (based on Process Mining book – Springer copyright 2011 and lecture material by Marlon.

Slides:



Advertisements
Similar presentations
Training Guide. `
Advertisements

/faculteit technologie management /faculteit wiskunde en informatica PM-1 Process mining: Discovering Process Models from Event Logs Prof.dr.ir. Wil van.
The Present Simple Tense: Often Usually Always Every day Sometimes.
Jorge Muñoz-Gama Josep Carmona
A university for the world real R © 2009, Chapter 3 Advanced Synchronization Moe Wynn Wil van der Aalst Arthur ter Hofstede.
Sequential Patterns & Process Mining Current State of Research Edgar de Graaf LIACS.
Process Mining in the Context of Web Services Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.
1 Process Mining Seminars in Software and Services for the Information Society Rome, 2014, May the 5th Francesco Leotta Dipartimento di Ingegneria Informatica,
/faculteit technologie management 1 Process Mining: Organizational and Conformance Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros.
Workflow Management Kap. 4. Analyzing Workflows Wil van der Aalst has copyrights to almost all figures in the following slideshow made by Lars Frank.
Appendix A The Future of Workflows Wil van der Aalst has copyrights to almost all figures in the following slideshow made by Lars Frank.
/faculteit technologie management 1 Process Mining: Control-Flow Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.
Process discovery: Inductive Miner
Aligning Event Logs And Declare Models for Conformance Checking Massimiliano de Leoni, Fabrizio Maggi Wil van der Aalst.
Data Conformance Checking using Optimal Alignments Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers.
Aligning Event Logs and Process Models for Multi- perspective Conformance Checking: An Approach Based on ILP Massimiliano de Leoni Wil M. P. van der Aalst.
Beyond Process Mining: Discovering Business Rules From Event Logs Marlon Dumas University of Tartu, Estonia With contributions from Luciano García-Bañuelos,
Block-Structured Process Discovery: Filtering Infrequent Behaviour Sander Leemans Dirk Fahland Wil van der Aalst Eindhoven University of Technology.
/faculteit technologie management Genetic Process Mining Ana Karla Medeiros Ton Weijters Wil van der Aalst Eindhoven University of Technology Department.
/faculteit technologie management Genetic Process Mining Ana Karla Alves de Medeiros Eindhoven University of Technology Department.
Process Mining in CSCW Systems All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei ( )
Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department.
/faculteit technologie management Dutch-Belgian Database Day 2007 The Challenges of Process Mining A.J.M.M. Weijters (and many others)
/faculteit technologie management Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance Wil van der Aalst.
Discovering Coordination Patterns using Process Mining Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department of Information and Technology.
Boudewijn van Dongen April 27, 2005 The ProM-framework A framework for integrating process mining tools.
/faculteit technologie management 1 Process Mining: General Introduction Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University of.
Boudewijn van Dongen June 22, 2004 /t Process Mining, the basics.
Process Mining: Discovering processes from event logs All truths are easy to understand once they are discovered; the point is to discover them. Galileo.
/faculteit technologie management Genetic Process Mining Wil van der Aalst Ana Karla Medeiros Ton Weijters Eindhoven University of Technology Department.
/faculteit technologie management 1 Process Mining: Extension Mining Algorithms Ana Karla Alves de Medeiros Ana Karla Alves de Medeiros Eindhoven University.
Process Mining for Ubiquitous Mobile Systems An Overview and a Concrete Algorithm Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology Department.
A university for the world real R © 2009, Chapter 14 EPCs Jan Mendling.
A university for the world real R © 2009, Chapter 23 Epilogue Wil van der Aalst Michael Adams Arthur ter Hofstede Nick Russell.
Insuring Sensitive Processes through Process Mining Jorge Munoz-Gama Isao Echizen Jorge Munoz-Gama and Isao Echizen.
Jorge Muñoz-Gama Universitat Politècnica de Catalunya (Barcelona, Spain) Algorithms for Process Conformance and Process Refinement.
Process Mining Control flow process discovery
Process Mining: Discovering processes from event logs All truths are easy to understand once they are discovered; the point is to discover them. Galileo.
Workflow Management introduktion: Wil van der Aalst has copyrights to the slides conserning his book about Workflow Management. However, some of the slides.
Data Mining Technical Committee (DMTC) Chair: Barbara Hammer Vice-Chairs: Carlotta Domeniconi Zhi-Hua Zhou Short TC Report for the 2013 June AdCom Meeting.
Jianmin Wang 1, Shaoxu Song 1, Xuemin Lin 2, Xiaochen Zhu 1, Jian Pei 3 1 Tsinghua University, China 2 University of New South Wales, Australia 3 Simon.
Jianmin Wang 1, Shaoxu Song 1, Xiaochen Zhu 1, Xuemin Lin 2 1 Tsinghua University, China 2 University of New South Wales, Australia 1/23 VLDB 2013.
Han-na Yang Rediscovering Workflow Models from Event-Based Data using Little Thumb.
Process-oriented System Analysis Process Mining. BPM Lifecycle.
Decision Mining in Prom A. Rozinat and W.M.P. van der Aalst Joosung, Ko.
/faculteit technologie management Workflow Mining: Current Status and Future Directions Ana Karla A. de Medeiros, W.M.P van der Aalst and A.J.M.M. Weijters.
Decomposing Data-aware Conformance Checking Massimiliano de Leoni, Jorge Munoz-Gama, Josep Carmona, Wil van der Aalst PAGE 0.
Process Mining – Concepts and Algorithms Review of literature on process mining techniques for event log data.
Beyond Tasks and Gateways: Discovering BPMN Models with subprocesses, boundary events and activity markers Raffaele Conforti, Marcello La Rosa Queensland.
The Automated Discovery of Hybrid Processes Fabrizio M. Maggi University of Tartu Tijs Slaats* IT University of Copenhagen Exformatics Hajo A. Reijers.
Multi-phase Process Mining: Building Instance Graphs
30 januari 2018 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.
7 mei 2018 Process Mining in CSCW Systems All truths are easy to understand once they are discovered; the point is to discover them. Galileo Galilei.
Process discovery Sander Leemans.
MTAT Business Process Management (BPM) Lecture 11: Process Monitoring and Mining Fabrizio Maggi (based on lecture material by Marlon Dumas, Wil.
Profiling based unstructured process logs
Exploring processes and deviations
David Redlich, Thomas Molka, Wasif Gilani, Awais Rashid, Gordon Blair
CSS 496 Business Process Re-engineering for BS(CS)
Genetic Algorithm and Their Applications to Scheduling
Concurrent Systems Modeling using Petri Nets – Part II
metaheuristic methods and their applications
Wil van der Aalst Eindhoven University of Technology
Service Perspectives in Process Mining
Decomposed Process Mining: The ILP Case
Metaheuristic methods and their applications. Optimization Problems Strategies for Solving NP-hard Optimization Problems What is a Metaheuristic Method?
Multi-phase process mining
3 mei 2019 Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance Wil van der Aalst Ana Karla A. de Medeiros.
5 juli 2019 Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance Wil van der Aalst Ana Karla A. de Medeiros.
19 augustus 2019 Mining Social Networks Uncovering interaction patterns in business processes Prof.dr.ir. Wil van der Aalst Eindhoven University of Technology.
Presentation transcript:

Process Mining Control flow process discovery Fabrizio Maria Maggi (based on Process Mining book – Springer copyright 2011 and lecture material by Marlon Dumas, Wil van der Aalst and Ana Karla Alves de Medeiros

Process Mining

Control-Flow Mining EventLog Discovery Techniques: Control-Flow Mining MinedModel 1.Start 2.Get Ready 3.Travel by Train 4.Beta Event Starts 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Travel by Train 1.Start 2.Get Ready 3.Travel by Train 4.Beta Event Starts 5.Give a Talk 6.Visit Brewery 7.Have Dinner 8.Go Home 9.Travel by Train 1.Start 2.Get Ready 3.Travel by Car 4.Beta Event Starts 5.Give a Talk 6.Visit Brewery 7.Have Dinner 8.Go Home 9.Pay Parking 10.Travel by Car 1.Start 2.Get Ready 3.Travel by Car 4.Conference Starts 5.Join Reception 6.Have Dinner 7.Go Home 8.Pay Parking 9.Travel by Car 10.End Start Get Ready Travel by Train Train Travel by Car Car Conference Starts Give a Talk Join Reception Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End

Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks

Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks + noise in logs!

α-algorithm Basic Idea: Ordering relations Direct succession: x>y iff for some case x is directly followed by y. Causality: x  y iff x>y and not y>x. Parallel: x||y iff x>y and y>x Unrelated: x#y iff not x>y and not y>x. case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B... A>BA>CB>CB>DC>BC>DE>F ABABACACBDBDCDCDEFEFABABACACBDBDCDCDEFEF B||CC||BABCDACBDEF

Basic Idea: Example

Basic Idea: Footprints

Basic Idea: Patterns

α-algorithm

α-algorithm: Applicative Example

Limitations: short loops of length 1 b>b and not b>b implies bb (impossible!)

Limitations: short loops of length 1 Example “Short1”

Limitations: short loops of length 2 c>b and b>c implies c||b and b||c instead of cb and bc

Limitations: short loops of length 2 Example “Short2”

Limitations: non-free-choice nets Example “nonlocal”

Limitations: invisible tasks Example “invisible”

Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks

Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks + noise in logs!

Heuristic Miner

Example “heuristic”

Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks

Mining Common Constructs Sequence Splits Joins Loops Non-Free Choice Invisible Tasks Duplicate Tasks PayParking GetReady Travel by Train Train Travel by Car Car Defense Starts Give a Talk Ask Question Defense Ends Go Home Travel by Train Train Travel by Car Car Have Drinks + noise in logs!

Genetic Miner

GPM – Fitness Measure Start Get Ready Travel by Train Train Travel by Car Car Conference Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End Guides the search!

Genetic Miner: Crossover

Genetic Miner: Mutation