Jeudi 27 Avril

Retour à la vue des calendrier
Jeudi 27 Avril
Heure: 12:30 - 13:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: RoSTBiDFramework: Optimised Framework based on Rough Set Theory for Big Data Pre- processing in Certain and Imprecise Contextsble
Description: Zeineb Chelly 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 Aberystwyth University. Further expertise is provided by internal
and external collaborators from academic and non-academic
institutions, namely Lebbah and Azzag (University of Paris 13), Shen
(University of Aberystwyth), Tino (University of Birmingham), Merelo
(University of Granada) and an industrial partner from France.