In recent years, predictive analytics solutions have been developed to forecast/predict the future based on the past. However, an even more powerful type of analytics is emerging. Prescriptive Analytics does not only predict the future, but also suggests (prescribes) the best course of action to take, given constraints, objectives, requirements, and parameters for a given business optimization scenario. Prescriptive analytics entails the steps of information collection, extraction, consolidation, visualization, forecasting, optimization, and what-if analysis. This supports effective decision making based on mathematical optimization and simulation.

However, building prescriptive analytics solutions with current tools is labor-intensive, error-prone, and inefficient, as a range of non-integrated and specialized tools have to be glued together in an ad-hoc fashion. This project will build a generic platform for prescriptive analytics for Big Data, which tighly integrates scalable data storage with declarative specification of queries, constraints, objectives, requirements, and mathematical optimizations in a unified framework which is both powerful and efficient, yet easy-to-use.

Main Advisor at Aalborg Universitet (AAU)
Co‐advisor at Technische Universität Dresden (TUD)