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Keynote Lectures

Mine Your Simulation Model: Automated Discovery of Business Process Simulation Models From Execution Data
Marlon Dumas, University of Tartu, Estonia

Available Soon
Peter Fritzson, Linköping University, Sweden

 

Mine Your Simulation Model: Automated Discovery of Business Process Simulation Models From Execution Data

Marlon Dumas
University of Tartu
Estonia
 

Brief Bio
Marlon Dumas is Professor of Information Systems at University of Tartu, Estonia and co-founder of Apromore - a company dedicated to developing and commercializing open-source process mining solutions. His research focuses on data-driven methods for business process management, including process mining and predictive process monitoring. He is currently recipient of an Advanced Grant from the European Research Council with the mission of developing algorithms for automated discovery and assessment of business process improvement opportunities. During his career, he has published over 200 research publications, 10 US/EU patents, and a textbook that is used in around 300 universities worldwide (Fundamentals of Business Process Management).


Abstract
Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This capability allows analysts to compare alternative options to  improve a business process. A common roadblock for business process simulation is the fact that constructing high-fidelity simulation models is cumbersome and error-prone.

Modern information systems such as Enterprise Resource Planning or Customer Relationship Management systems store detailed execution logs of the business processes they support. These execution logs can be used to automatically discover simulation models. However, discovering high-accuracy simulation models from business process execution data turns out to be a challenging problem due to the numerous factors that affect the performance of real processes. One of the major challenges is accounting for various work patterns, including multitasking, task prioritization, batching, resource availability schedules, and time-varying resource performance (e.g. fatigue effects).

In this talk, I will give an overview of recent research in the field of automated discovery of business process simulation models. I will outline two approaches: one that uses process mining, curve fitting, and Bayesian optimization to discover and enhance a process model from an event log, and another approach that combines process mining with deep learning techniques. I will discuss the relative merits of these approaches and sketch open research challenges and questions.



 

 

Keynote Lecture

Peter Fritzson
Linköping University
Sweden
 

Brief Bio
Available Soon


Abstract
Available Soon



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