Publications

The Horizon 2020 Blue-Cloud (B-C) project launched in October 2019, aiming to demonstrate the potential of web-based Open Science in the marine domain.

The main goal of Blue-Cloud WP5 is to deliver and maintain a Communication, Marketing & Dissemination Plan and Strategy for the pan-European rollout of the Blue-Cloud services.

The Blue-Cloud Service platform will feature a variety of services that can be used for undertaking world-class science via the European Open Science Cloud (EOSC) framework, by featuring leading operational marine research infrastructures and e-infrastructures.

The Blue-Cloud project is developing a cyber platform bringing together and providing access to multidisciplinary data from observations and models, analytical tools, and computing facilities essential to support research to understand better and manage the many aspects of ocean sustainability.

Quality Critical Decentralised Applications (QCDApp) have high requirements for system performance and service quality, involve heterogeneous infrastructures (Clouds, Fogs, Edges and IoT), and rely on the trustworthy collaborations among participants of data sources and infrastructure providers t

As part of the EU H2020 Blue-Cloud project activities are undertaken for developing and deploying a Blue-Cloud cyber infrastructure with smart federation of multidisciplinary data repositories, analytical tools, and computing facilities.

The objective of Blue Cloud is to federate access to some data services at EU level, to ease discovery and enable cross-discipline work. The data access and discovery service covers the following infrastructures:

The Blue-Cloud project plans to pilot a cyber platform bringing together and providing access to multidisciplinary data from observations and models, analytical tools, and computing facilities essential to support research to understand better and manage the many aspects of ocean sustainability.<

Data intensive workflow applications are characterized by their continuously growing volumes of data being processing, complexity of tasks in the pipeline, and infrastructure capacity required for computation and storage.

In this paper, a non‐negative matrix factorization feature

expansion (NMFFE) approach was proposed to

overcome the feature‐sparsity issue when expanding

features of short‐text. First, we took the internal relationships

of short texts and words into account when