/faculteit technologie management www.processmining.org Genetic Process Mining Ana Karla Alves de Medeiros Eindhoven University of Technology Department.

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Presentation transcript:

/faculteit technologie management Genetic Process Mining Ana Karla Alves de Medeiros Eindhoven University of Technology Department of Information Systems

/faculteit technologie management Outline Process Mining –Motivation –Current Techniques –Contribution Genetic Algorithms Experiments and Results Conclusion and Future Work

/faculteit technologie management Outline Process Mining –Motivation –Current Techniques –Contribution Genetic Algorithms Experiments and Results Conclusion and Future Work

/faculteit technologie management Motivation – Typical way to deploy a system 1.Get Ready 2.Travel by Train 3.Beta Event Starts 4.Visit Brewery 5.Have Dinner 6.Go Home 7.Travel by Train 1.Get Ready 2.Travel by Train 3.Beta Event Starts 4.Give a Talk 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Travel by Train

/faculteit technologie management Motivation – Typical way to deploy a system Time consuming –Paper procedures –Meetings Error prone –Different people have different views about a same process –Information about the process may be incomplete

/faculteit technologie management Motivation – More possible cases in the log 1.Get Ready 2.Travel by Train 3.Beta Event Starts 4.Visit Brewery 5.Have Dinner 6.Go Home 7.Travel by Train 1.Get Ready 2.Travel by Train 3.Beta Event Starts 4.Give a Talk 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Travel by Train 1.Get Ready 2.Travel by Car 3.Beta Event Starts 4.Give a Talk 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Pay Parking 9.Travel by Car 1.Get Ready 2.Travel by Car 3.Beta Event Starts 4.Visit Brewery 5.Have Dinner 6.Go Home 7.Pay Parking 8.Travel by Car

/faculteit technologie management Motivation – Process Mining 1.Get Ready 2.Travel by Train 3.Beta Event Starts 4.Visit Brewery 5.Have Dinner 6.Go Home 7.Travel by Train 1.Get Ready 2.Travel by Train 3.Beta Event Starts 4.Give a Talk 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Travel by Train 1.Get Ready 2.Travel by Car 3.Beta Event Starts 4.Give a Talk 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Pay Parking 9.Travel by Car 1.Get Ready 2.Travel by Car 3.Beta Event Starts 4.Visit Brewery 5.Have Dinner 6.Go Home 7.Pay Parking 8.Travel by Car Start Get Ready Travel by Train Train Travel by Car Car Beta Event Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End Log MiningTechniques MinedModel

/faculteit technologie management Motivation – Process Mining Before deployment –Objective picture of how the process has been executed After deployment –Feedback mechanism

/faculteit technologie management Current Techniques vs Constructs Sequence Splits Joins Loops Non-free Choice Invisible Tasks Duplicate Tasks Start Get Ready Travel by Train Train Travel by Car Car Beta Event Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End

/faculteit technologie management Current Techniques vs Constructs Sequence Splits Joins Loops Non-free Choice Invisible Tasks Duplicate Tasks Start Get Ready Travel by Train Train Travel by Car Car Beta Event Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End    1.Get Ready 2.Travel by Car 3.Beta Event Starts 4.Give a Talk 5.Visit Brewery 6.Have Dinner 7.Go Home 8.Pay Parking 9.Travel by Car

/faculteit technologie management Current Techniques vs Constructs Sequence Splits Joins Loops Non-free Choice Invisible Tasks Duplicate Tasks Start Get Ready Travel by Train Train Travel by Car Car Beta Event Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End   

/faculteit technologie management Current Techniques vs Constructs Sequence Splits Joins Loops Non-free Choice Invisible Tasks Duplicate Tasks Start Get Ready Travel by Train Train Travel by Car Car Beta Event Starts Give a Talk Visit Brewery Have Dinner Go Home Travel by Train Train Travel by Car Car PayParking End   

/faculteit technologie management Current Techniques vs Constructs: The  -algorithm

/faculteit technologie management Current Techniques Problematic structural constructs –Non-free choice, invisible tasks, duplicate tasks Noise –Wrongly logged traces –Exceptional situations Can we develop a mining algorithm that is able to tackle all structural constructs and noisy logs at the same time?

/faculteit technologie management Outline Process Mining –Motivation –Current Techniques –Contribution Genetic Algorithms Experiments and Results Conclusion and Future Work

/faculteit technologie management Genetic Algorithms –Global approach local optimum global optimum

/faculteit technologie management Genetic Process Mining (GPM) Aim: Use genetic algorithm to tackle non-free choice, invisible tasks, duplicate tasks and noise. Internal Representation Fitness Measure Genetic Operators

/faculteit technologie management Genetic Process Mining (GPM) Demo –Genetic Miner plug-in –Tool at

/faculteit technologie management Outline Process Mining –Motivation –Current Techniques –Contribution Genetic Algorithms Experiments Conclusion and Future Work

/faculteit technologie management Experiments Simulation to generate the logs Original Process aaaaaaaaaa aaaaaaaaaa aaaaaaaaaa aaaaa Logs Genetic Miner aaaaaaaaaa aaaaaaaaaa aaaaaaaaaa aaaaa Mined Process Can the mined process generate all the behavior in the log? How close is the behavior of the mined process to the original one?

/faculteit technologie management Outline Process Mining –Motivation –Current Techniques –Contribution Genetic Algorithms Experiments and Results Conclusion and Future Work

/faculteit technologie management Conclusion and Future work Genetic algorithms can be used to mine process models –Global approach –Robust to noise Run more experiments Case study –Do you have real data for us? Write thesis

/faculteit technologie management