The Big Data era poses a critically difficult challenge and striking development opportunities in High-Performance Computing (HPC): how to efficiently turn massively large data into valuable information and meaningful knowledge. Computationally effective HPC is required in a rapidly-increasing number of data-intensive domains, such as Life and Physical Sciences, and Socioeconomic Systems. Modelling and Simulation (MS) […]
There is no escape from the expansion of information, so that structuring and locating meaningful knowledge becomes ever more difficult. This project will tackle this urgent problem using the unique networking and capacity-building features provided by the COST framework. For the first time, a platform will be created where information professionals, sociologists, physicists, digital humanities
Develop improved methods for knowledge extraction from massive amounts of data regarding moving objects. This knowledge is essential to substantiate decision making in public and private sectors. Moving object data typically include trajectories of concrete objects (e.g. humans, vehicles, animals, and goods), as well as trajectories of abstract concepts (e.g. spreading diseases). While movement records
COST Action IC0804 proposed realistic energy-efficient alternate solutions to share IT distributed resources. As large scale distributed systems gather and share more and more computing nodes and Storage resources, their energy consumption is exponentially increasing. While much effort is nowadays put into hardware specific solutions to lower energy consumptions, the need for a complementary approach
COST project on Energy efficiency in large scale distributed systems Read More »
