Information Sytems (IS)

Main research interest (DSC/e related)

Data mining, process mining, machine learning and computational intelligence methods are essential to design information systems for intelligent decision support, so that organizations can fulfill their goals of operational excellence and improved decision making. For this purpose, our group develops methods, techniques and tools for advanced analysis of business processes and optimal data-driven decision making in their execution. The group’s research centers on computational intelligence methods for decision models in which qualitative, linguistic information can be combined with quantitative, numerical information from data. Research involves the following topics: 

  • data-driven logistics, healthcare analytics, retail operations
  • computational intelligence (fuzzy systems, neural nets, evolutionary computation)
  • business applications of data mining and machine learning
  • data-intensive business process optimization and services development

Success stories

The practical relevance of the context-aware, adaptive decision support systems that we develop are studied in industry cases from e-commerce, logistics and healthcare. For example, the GET Service (Green European Transportation Service) project provided transportation planners with the means to plan transportation routes more efficiently and to respond quickly to unexpected events during transportation. Two startup companies originated from the project.   

In another project, machine learning predictive models for decision support have been studied in intensive care units of multiple health centers in different countries.

Project examples

  • DaiPeX – Dinalog (Dutch Top Institute on Logistics)
    New algorithms and software that can handle time-dependent, stochastic planning problems, based on high-volume information in Cross Chain Control Centers (4C). 
  • GameBus – EIT Digital
    Valorization focused project to stimulate physical, cognitive and social healthy behavior across communities and generations of people. 
  • Continuous Personal Health – Philips, TU/e Flagship.
    Develop data-driven, predictive solutions for the whole care continuum. 
  • Clinical Pathway Analysis – Philips, TU/e, Zhejiang University BrainBridge Program
    Develop tools to analyze and study the performance of clinical pathways and clinical workflows.