Obiettivo "Over the last decades, the amount of data has increased in an unprecedented rate, leading to a new terminology: ""Big Data"". Big data are specified by their Volume, Variety, Velocity and by their Veracity/Imprecision. Based on these 4V specificities, it has become difficult to quickly acquire the most useful information from the huge amount of data at hand. Thus, it is necessary to perform data (pre-)processing as a first step. In spite of the existence of many techniques for this task, most of the state-of-the-art methods require additional information for thresholding and are neither able to deal with the big data veracity aspect nor with their computational requirements. This project's overarching aim is to fill these major research gaps with an optimised framework for big data pre-processing in certain and imprecise contexts. Our approach is based on Rough Set Theory (RST) for data pre-processing and Randomised Search Heuristics for optimisation and will be implemented under the Spark MapReduce model.The project combines the expertise of the experienced researcher Dr Zaineb Chelly Dagdia in machine learning, rough set theory and information extraction with the knowledge in optimisation and randomised search heuristics of the supervisor Dr Christine Zarges at the University of Birmingham (UoB). Further expertise is provided by internal and external collaborators from academic and non-academic institutions, namely Prof Tino (UoB), Prof Merelo (University of Granada), Prof Lebbah (University of Paris 13) and Philippe Barra (Arrow Group). The involvement of Arrow Group, an SME based in France specialised in Big data, Banking, Finance & Insurance is of particular importance to ensure that real-world requirements are met throughout the development of the framework." Campo scientifico natural sciencesmathematicspure mathematicsdiscrete mathematicsmathematical logicnatural sciencescomputer and information sciencesdata sciencebig datanatural sciencescomputer and information sciencesdata sciencedata miningnatural sciencescomputer and information sciencesartificial intelligenceheuristic programmingnatural sciencesmathematicsapplied mathematicsmathematical model Programma(i) 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 Argomento(i) MSCA-IF-2015-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF) Invito a presentare proposte H2020-MSCA-IF-2015 Vedi altri progetti per questo bando Meccanismo di finanziamento MSCA-IF-EF-ST - Standard EF Coordinatore ABERYSTWYTH UNIVERSITY Contribution nette de l'UE € 183 454,80 Indirizzo VISUALISATION CENTRE PENGLAIS SY23 3BF Aberystwyth Regno Unito Mostra sulla mappa Regione Wales West Wales and The Valleys South West Wales Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 183 454,80 Partecipanti (1) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto THE UNIVERSITY OF BIRMINGHAM Partecipazione conclusa Regno Unito Contribution nette de l'UE € 0,00 Indirizzo Edgbaston B15 2TT Birmingham Mostra sulla mappa Regione West Midlands (England) West Midlands Birmingham Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale Nessun dato
