Predictive Monitoring of Business Processes
Predictive business process monitoring is concerned with predicting future states or properties of ongoing executions of a business process, based on past executions thereof. Such predictions can range from predicting which activity will be performed next, when, and who will perform it, to predicting the remaining execution time or the final outcome of the process. For example, in an order-to-cash process, predictive monitoring techniques can be used to predict how likely is it that a purchase order will be fulfilled on time, or how likely is it that the customer will be satisfied after fulfillment of the order. In this talk, we will present a framework for conceptualizing and addressing predictive process monitoring problems using various machine learning techniques, ranging from classical classification techniques (e.g. random forests), to Hidden Markov Models and Recurrent Neural Networks. We will also present an empirical evaluation of the relative performance of these techniques and discuss their relative applicability and limitations.
The seminar will be given in Aula A6 at 14:15 on Friday 28 April 2017
Marlon Dumas is Professor of Software Engineering at University of Tartu, Estonia. Prior to this appointment he was faculty member at Queensland University of Technology and visiting researcher at SAP Research, Australia. His research interests span across the fields of software engineering, information systems and business process management. His ongoing work focuses on combining data mining and formal methods for analysis and monitoring of business processes. He has published extensively in conferences and journals across the fields of software engineering and information systems. He is co-inventor of seven granted US/EU patents and co-author of two textbooks in the field of business process management.