Objetivo Sequential data are everywhere, from DNA sequences to astronomical light curves, and from aircraft engine monitoring data to the prices of stock options. Recent advances in various fields such as those of data storage, networking and sensing technologies, have allowed organizations to gather overwhelming amounts of sequential data at unprecedented speeds.This wealth of information enables analysts to identify patterns, find abnormalities, and extract knowledge. It is noteworthy that common practice in various domains is to use custom data analysis solutions, usually built using higher level programming languages, such as R/Python. Such techniques, however, while commonly acceptable in small data processing scenarios, are unfit for larger scale data management and exploration. This is because they come in contrast to all previous database research, not taking advantage of indexes, physical data independence, query optimization, and data processing methods, designed for scalability. In these domains, database systems are used merely for storing and retrieving data and not as the sophisticated query processing systems they are.Current relational storage layers cannot handle the access patterns that analysts of sequential data are interested in, without scanning large amounts of unnecessary data or without large processing overhead. Thus, making complex analytics inefficient.In order to exploit this new opportunity, we plan to develop specialized data series storage and retrieval systems, which will allow analysts – across different fields – to efficiently manipulate the sequences of interest.The proposed research project, named NESTOR (Next gEneration Sequence sTORage), has the potential of great economic and social impact in Europe as multiple scientific and industrial fields are currently in need of the right tools, in order to handle their massive collections of data series. Ámbito científico natural sciencescomputer and information sciencessoftwarenatural sciencescomputer and information sciencesdatabasesnatural sciencesbiological sciencesgeneticsDNAengineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraftnatural sciencescomputer and information sciencesdata sciencedata processing Programa(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Tema(s) MSCA-IF-2016 - Individual Fellowships Convocatoria de propuestas H2020-MSCA-IF-2016 Consulte otros proyectos de esta convocatoria Régimen de financiación MSCA-IF-GF - Global Fellowships Coordinador UNIVERSITE PARIS CITE Aportación neta de la UEn € 246 668,40 Dirección 85 BD SAINT GERMAIN 75006 Paris Francia Ver en el mapa Región Ile-de-France Ile-de-France Paris Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 246 668,40 Socios (1) Ordenar alfabéticamente Ordenar por aportación neta de la UE Ampliar todo Contraer todo Socio Las organizaciones asociadas contribuyen a la aplicación de la acción, pero no firman el acuerdo de subvención. PRESIDENT AND FELLOWS OF HARVARD COLLEGE Estados Unidos Aportación neta de la UEn € 0,00 Dirección MASSACHUSETTS AVENUE 1350 02138 Cambridge Ver en el mapa Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 160 130,40