Keynote Lectures

Knowledge Engineering through Process Mining - The Practice

Linda Terlouw ICRIS Consultancy, Antwerp Management School, Avans University of Applied Sciences, Nyenrode Business University Netherlands

Brief Bio
Dr. ir. Linda Terlouw holds both an MSc in Computer Science and an Msc in Business Information from the University of Twente. Her PhD research focused on modularization of organizations and IT systems using Enterprise Ontology and Service-Oriented Architecture. At the moment she is mainly working on data science (e.g. forecasting), data visualization and process mining (www.processminingfactory.com). Before she started her own company, Icris, she worked for IBM and Ordina (a large Dutch consulting firm). Clients she has been working for include several Dutch water suppliers, a large municipality, several factories, and the Ministry of Defense. She is lector (professor) at the Avans University of Applied Sciences and teaches at Nyenrode Business University and Antwerp Management School.

Abstract

Large organizations often lack insight into the performance of their business processes, making it difficult to improve them. Process mining is an interdisciplinary field combining techniques from business process management and data science. It can, for instance, be used to check compliance of processes and to gain insight into bottlenecks in business processes. This lecture focuses on the problems and successes encountered in the practice of process mining. The main message is illustrated by various practical experiences.

The Role of Software Operation Knowledge in Software Ecosystems

Slinger Jansen Utrecht University Netherlands

Brief Bio

Slinger Jansen is a senior researcher at the Department of Information and Computer Science at Utrecht University. He is one of the leading researchers in the domain of software ecosystems and co-founders of the International Conference on Software Business, the Workshop on Ecosystem Architecture, and the International Workshop on Software Ecosystems. He is lead editor of the book “Software Ecosystems: Analyzing and Managing Business Networks in the Software Industry” and of several others. Besides his academic endeavors he actively supports new enterprises and sits on the boards of advisors of several start-ups, one of which is ThinkEcosystems.com.

Abstract
Increasingly, software producing organizations collaborate in networks that have become known as software ecosystems. The intricate structures of platforms upon platforms enable rapid innovation like never before. In this talk, we explore how these platforms collect knowledge about the platform itself, the applications running on it, and the end-users that make use of these applications. Through examples and case studies is shown that software operation knowledge in software ecosystems is essential for creating better software, happier users, and more productive developers.

Type-2 Fuzzy Systems for Human Decision Making

Jonathan Garibaldi University of Nottingham United Kingdom

Brief Bio
Professor Jon Garibaldi received the BSc degree in Physics from University of Bristol, UK, in 1984, and MSc degree and PhD degree from the University of Plymouth, UK, in 1990 and 1997, respectively. Prof. Garibaldi is currently Head of School of Computer Science, University of Nottingham, Head of the Intelligent Modelling and Analysis (IMA) Research Group, Member of the Lab for Uncertainty in Data and Decision Making (LUCID) and joint Director of the Advanced Data Analysis Centre (ADAC). His main research interests include modelling uncertainty and variation in human reasoning, and in modelling and interpreting complex data to enable better decision making, particularly in medical domains. Prof. Garibaldi is the current Editor-in-Chief of IEEE Transactions on Fuzzy Systems. He has served regularly in the organising committees and programme committees of a range of leading international conferences and workshops, such as FUZZ-IEEE, WCCI, EURO and PPSN.

Abstract
Type-2 fuzzy sets and systems, including both interval and general type-2 sets, are now firmly established as tools for the fuzzy researcher that may be deployed on a wide range of applications and in a wide set of contexts. However, in many situations the output of type-2 systems are type-reduced and then defuzzified to an interval centroid, which are then often even simply averaged to obtain a single crisp output. Many successful applications of type-2 have been in control contexts, often focussing on reducing the RMSE. This is not taking full advantage of the extra modelling capabilities inherent in type-2 fuzzy sets. In this talk, I will present some of the current research being carried out within the LUCID group at Nottingham, and wider, into type-2 for modelling human reasoning. I will cover approaches and methodologies which make more use of type-2 capabilities, illustrating these with reference to practical applications such as classification of breast cancer tumours, modelling expert variability in cyber-security contexts, and other decision support problems.