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 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 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.
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Start: Feb. 2, 2018, midnight
Start: Feb. 26, 2018, 4 p.m.
April 12, 2018, midnight
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