BDA.1. Seamlessly Integrating Data Warehouses and Forecast Models

The world is fast becoming populated with sensors, e.g., temperature, noise, or RFID sensors, continously emitting enourmous amounts of data. Along with the sensor data, even more data is being accumulated in traditional data warehouses. It is of paramount importance that all this data can be integrated and queried to answer analytical questions about the past, present, and (expected) future state of the world. The vision is that data from sensors (present state), data warehouses (past state), and forecast/prediction models (future state, obtained through data mining) are available through a unified infrastructure and can be queried seamlessly. This involves work on data modeling, uncertainty management, and query processing and optimisation techniques. The techniques to be developed in this respect should be general to be used in many specific domains, e.g., smart grids or the public sector.

Main Advisor at Aalborg Universitet (AAU)
Co-advisor at Technische Universit├Ąt Dresden (TUD)