The objective of this inducement prize is to improve the performance of software for the forecasting of geospatio-temporal data (collections of time-stamped records that are linked to a geospatial location). The prize will reward a solution which improves existing methods in terms of scalability, accuracy, speed and use of computational resources.
Many domains of societal or industrial significance, from epidemiology, to climate change, to transportation to energy production and transmission benefit from our ability to examine historical records and predict how the system under study will evolve.
In all these cases, it is not sufficient for predictions be accurate: they also need to be delivered fast enough for corrective action to be applied on the system observed.
The solution selected will demonstrate the ability to analyse extremely large scale collections of structured or multimedia geospatial temporal data in a way that is sensitive to the trade-off between the consumption of computational resources and the practical value of the predictions obtained. Datasets will consist of video collections and time-series recording weather conditions and parameters of energy grid operations.
This will not only result in the more efficient management of those domains in which spatio-temporal predictions are already used, but also in the applications of such predictive methods where today they are not, due to current limitations of speed, scalability, accuracy and resource efficiency. Possible domains of application include but are not limited to logistics, manufacturing, telecommunications.
This inducement prize also complements the activities of the Big Data cPPP which aims to develop Europe's data driven economy and the prospects offered by Big Data technologies (as outlined in the Communication[[http://ec.europa.eu/newsroom/dae/document.cfm?action=display&doc_id=6210]] adopted on July 2nd 2014).