Projects

DART (Data-driven AiRcraft Trajectory prediction research) addresses the topic “ER-02-2015 – Data Science in ATM” exploring the applicability of data science and complexity science techniques to the ATM domain. DART delivers an understanding on the suitability of applying big data and agent –based modelling techniques for predicting aircraft trajectories based on data-driven models and accounting for ATM network complexity effects, considering multiple correlated trajectories.

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The goal of the AMINESS project is to contribute in the safety, management and monitoring of the sea environment and the Aegean Sea in particular.
Reducing the possibility of ship accidents in the Aegean Sea is important to all economic, environmental, and cultural sectors of Greece. Oil spill cleanups can cost over 1 billion Euros, whereas accidents involving water soluble cargos would result in irrevocable changes to the Aegean ecosystem. Despite an increase in traffic, there are no national-level monitoring policies and ships formulate routes according to their best judgment. However, to reduce their own financial risk, shipping companies would directly benefit from a system that can reduce the possibility of an accident involving their own ship.

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As the trend to open up data and provide them freely on the Internet intensifies, the opportunities to create added value by combining and cross-indexing heterogeneous data at a large scale increase. To seize these opportunities we need infrastructure that is not only efficient, real-time responsive and scalable but is also flexible and robust enough to welcome data in any schema and form and to transparently relegate and translate queries from a unifying end-point to the multitude of data services that make up the open data cloud.

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