Zaineb Dagdia BSc(2008), MSc(2010), PhD(2014)
Room Number..........:  E51
Phone.......................:   +44 (0)1970 622410
Home Page...............:   Personal
Zaineb Chelly Dagdia is a computer scientist who, thanks to her Marie-Skłodowska-Curie Individual European Fellowship, is currently developing her research on an optimized framework for Big Data pre-processing in certain and imprecise contexts at Aberystwyth University, Wales, UK.
Her research interests include different aspects of Artificial Intelligence. She writes on evolutionary algorithms and artificial immune systems (AIS). She deals with reasoning under uncertainty and focuses on developing new AIS methods within an imprecise framework based on machine learning techniques and mathematical theories. She also extended her domain of expertise by dealing with Big Data. Her career publications include 4 journal papers (SCIindexed), 19 refereed and ranked conference proceedings papers and 2 book chapters (Springer). She was awarded the IEEE Young Researcher First Price, the ACM-W Award and last but not least the Marie-Skłodowska-Curie Individual European Fellowship.
- RoSTBiDFramework [Project Website]:
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 Prof Lebbah (University of Paris 13), Prof Shen (University of Aberystwyth), Prof Tino (University of Birmingham), Prof Merelo (University of Granada) and an industrial partner from France.
A Distributed Rough Set Theory based Algorithm for an Efficient Big Data Pre-processing under the Spark Framework. 2017 IEEE International Conference on Big Data(BigData 2017).2017 (In press).