Objective 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. Fields of science natural sciencescomputer and information sciencessoftwarenatural sciencescomputer and information sciencesdatabasesnatural sciencesbiological sciencesgeneticsDNAengineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraftnatural sciencescomputer and information sciencesdata sciencedata processing Programme(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 Topic(s) MSCA-IF-2016 - Individual Fellowships Call for proposal H2020-MSCA-IF-2016 See other projects for this call Funding Scheme MSCA-IF-GF - Global Fellowships Coordinator UNIVERSITE PARIS CITE Net EU contribution € 246 668,40 Address 85 bd saint germain 75006 Paris France See on map Region Ile-de-France Ile-de-France Paris Activity type Higher or Secondary Education Establishments Links Contact the organisation 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 Partners (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all Partner Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement. PRESIDENT AND FELLOWS OF HARVARD COLLEGE United States Net EU contribution € 0,00 Address Massachusetts avenue 1350 02138 Cambridge See on map 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 € 160 130,40
