Predictive Analytics on Data Streams
In today’s digital society, the Internet of Things (IoT) is producing significant quantities of data at an unprecedented rate, e.g. in manufacturing or health care.
The omnipresence of sensors and connectivity generates an explosive growth of data that offers the potential for a revolutionary transformation. The analysis of data streams coming from sensors and mobile devices is nowadays still underexplored and offers a lot of exciting opportunities to enrich systems and make them smarter by running data-driven applications.
More specifically, predictive analytics is considered to be one of the key drivers of innovation in the working of such systems. The functioning of these systems can greatly benefit from insights and actionable knowledge that can be extracted out of this data, such as events or anomalies.
Despite the availability of large quantities of data, data processing technologies based on machine learning have not quite matured equally and the transformation of data into actionable knowledge remains a complex task.
Staff
Tom Dhaene, Dirk Deschrijver, Erik Mannens, Azarakhsh Jalavand
Researchers
Ivo Couckuyt, Joeri Ruyssinck, Roberto Medico, Dieter De Witte, Dieter De Paepe, Femke Ongenae, Gilles Vandewiele
Projects
- SBO HYMOP: “HYMOP: hypermodelling strategies on multi-stream time-series data for operational optimization”
- ICON SENCOM: “Smart energy consumption in manufacturing”
- EU H2020 AUTOWARE: “Wireless Autonomous, Reliable and Resilient ProductIon Operation ARchitecture for Cognitive Manufacturing”
- "On-going bilateral collaboration with Ghent University Hospital on predictive analytics for Big Intensive Care data"
- SBO SMILE-IT: Stable MultI-agent LEarnIng for neTworks
Key publications
- Joachim van der Herten, Frederick Depuydt, Leen De Baets, Dirk Deschrijver, Matthias Strobbe, Chris Develder, R Bruneliere and J-W Rombouts, “Energy flexibility assessment of an industrial coldstore process”, IEEE International Energy Conference. p.1-6, 2016.
- Xu Gong, Jens Trogh, David Plets, Emmeric Tanghe, Quentin Braet, Prashant Singh, Jeroen Hoebeke, Dirk Deschrijver, Tom Dhaene and Luc Martens, et al., “Measurement-based wireless network planning, monitoring, and reconfiguration solution for robust radio communications in indoor factories“, IET Science measurement & technology, 2016.
- R. Houthooft, J. Ruyssinck, J. van der Herten, S. Stijven, I. Couckuyt, B. Gadeyne, F. Ongenae, K. Colpaert, J. Decruyenaere, T. Dhaene, F. De Turck, Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores, Artif Intell Med. 2015 Mar; 63(3): pp. 191-207
- Joeri Ruyssinck, Joachim van der Herten, Rein Houthooft, Femke Ongenae, Ivo Couckuyt, Bram Gadeyne, Kirsten Colpaert, Johan Decruyenaere, Filip De Turck, Tom Dhaene, Random Survival Forests for Predicting the Bed Occupancy in the Intensive Care Unit, Computational and Mathematical Methods in Medicine, 2016.
