The public sector provides essential backbone services for the society, but is increasingly under a dual pressure of budget cuts and an aging population. The only way of solving this seemingly impossible problem is to use the available resources more intelligently. Much administrative data already exist in the public sector (and more and more of it is becoming publicly available, e.g., the Danish authorities are actively working on publishing their data sets and have already published 822 data sets including the land register and employment statistics), and more will be collected as smart devices and sensors are rolled out to the population, e.g., to provide care for elderly living at home. Using the advanced data models from Challenge 1 and the common advanced platform, an integrated data warehouse (iDW) will be created. Here, data is harmonised and stored, allowing effective and cross‐cutting analysis on diverse and complex data. Second, novel techniques for knowledge discovery (data mining) in complex heterogeneous public data will be developed, allowing the discovery of previously unknown correlations and links, e.g., between sensor values and treatments for a user, or similarities between certain citizen groups. Another key aspect is the advanced use of forecasted data which entails a huge potential for the public sector. The research is done in close collaboration with the public sector associated partners, who will supply relevant anonymised data and act as trial users of the developed prototypes, thus influencing the further research.

Main Advisor at Aalborg Universitet (AAU)
Co-advisor at Poznan University of Technology (PUT)