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Optimised Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts

Total Cost €


EC-Contrib. €






Project "RoSTBiDFramework" data sheet

The following table provides information about the project.


Organization address
postcode: SY23 3BF

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website
 Total cost 183˙454 €
 EC max contribution 183˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-03-01   to  2019-02-28


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

'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. '


year authors and title journal last update
List of publications.
2019 Zaineb Chelly Dagdia and Zied Elouedi
A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells
published pages: , ISSN: 1432-1343, DOI:
Journal of Classification 2019-09-16
2018 Zaineb Chelly Dagdia
Optimized Framework based on Rough Set Theory for Big Data Pre-processing in Certain and Imprecise Contexts” -- Marie Sklodowska-Curie Project: Open Problems’
published pages: , ISSN: , DOI: 10.4230/DagRep.7.9.62
Recent Trends in Knowledge Compilation (Dagstuhl Seminar 17381) 2019-09-16
2018 Zaineb Chelly Dagdia, Christine Zarges, Gael Beck, Mustapha Lebbah
Modèle de Sélection de Caractéristiques pour les Données Massives
published pages: 1--12, ISSN: , DOI:
15ème édition de l\'atelier Fouille de Données Complexes 2019-09-16
2018 Zaineb Chelly Dagdia
Optimized Framework based on Rough Set Theory for Big Data Preprocessing in Certain and Imprecise Contexts
published pages: , ISSN: , DOI:
The 5th MCAA Annual Conference and General Assembly 2019-09-16
2018 Zaineb Chelly Dagdia
A scalable and distributed dendritic cell algorithm for big data classification
published pages: 1-13, ISSN: 2210-6502, DOI: 10.1016/j.swevo.2018.08.009
Swarm and Evolutionary Computation 2019-09-16

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