Objective "Future data processing challenges in science will enter the ""Big Data"" era, involving massive, as well as complex and heterogeneous data. Extracting, with high precision, every bit of information from scientific data requires overcoming fundamental statistical challenges, which mandate the design of dedicated methods that must be both effective enough to capture the intricacy of real-world datasets and robust to the high complexity of instrumental measurements. Moreover, future datasets, such as those provided by the space mission Euclid, will involve at least gigascale data, which will make mandatory the development of new, physically relevant, data models and the implementation of effective and computationally efficient processing tools. The recent emergence of novel data analysis methods in machine learning should foster a new modeling framework, allowing for a better preservation of the intrinsic physical properties of real data that generally live on intricate spaces, such as signal manifolds. Furthermore, advances in operations research and optimization theory pave the way for effective solutions to overcome the large-scale data processing bottlenecks. In this context, the objective of the DEDALE project is threefold: i) introduce new models and methods to analyze and restore complex, multivariate, manifold-based signals; ii) exploit the current knowledge in optimization and operations research to build efficient numerical data processing algorithms in the large-scale settings; and iii) show the reliability of the proposed data modeling and analysis technologies to tackle Scientific Big Data challenges in two different applications: one in cosmology, to map the dark matter mass map of the universe, and one in remote sensing to increase the capabilities of automatic airborne imaging analysis systems." Fields of science natural sciencescomputer and information sciencesdata sciencebig datanatural sciencesphysical sciencesastronomyastrophysicsdark matternatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencesphysical sciencesastronomyphysical cosmologynatural sciencescomputer and information sciencesdata sciencedata processing Keywords wavelets dictionary learning manifold weak lensing remote sensing Programme(s) H2020-EU.1.2. - EXCELLENT SCIENCE - Future and Emerging Technologies (FET) Main Programme H2020-EU.1.2.1. - FET Open Topic(s) FETOPEN-RIA-2014-2015 - FET-Open research projects Call for proposal H2020-FETOPEN-2014-2015 See other projects for this call Sub call H2020-FETOPEN-2014-2015-RIA Funding Scheme RIA - Research and Innovation action Coordinator COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES Net EU contribution € 857 875,00 Address Rue leblanc 25 75015 Paris France See on map Region Ile-de-France Ile-de-France Paris Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 Participants (4) Sort alphabetically Sort by Net EU contribution Expand all Collapse all IDRYMA TECHNOLOGIAS KAI EREVNAS Greece Net EU contribution € 560 000,00 Address N plastira str 100 70013 Irakleio See on map Region Νησιά Αιγαίου Κρήτη Ηράκλειο Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 SAFRAN ELECTRONICS & DEFENSE France Net EU contribution € 288 125,00 Address 2 boulevard du general martial valin 75015 Paris See on map Region Ile-de-France Ile-de-France Paris Activity type Private for-profit entities (excluding Higher or Secondary Education Establishments) Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 UNIVERSITY COLLEGE LONDON United Kingdom Net EU contribution € 485 397,50 Address Gower street WC1E 6BT London See on map Region London Inner London — West Camden and City of London Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00 TECHNISCHE UNIVERSITAT BERLIN Germany Net EU contribution € 511 000,00 Address Strasse des 17 juni 135 10623 Berlin See on map Region Berlin Berlin Berlin Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
