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

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.
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Topic(s)

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Funding Scheme

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MSCA-IF-EF-ST - Standard EF

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Call for proposal

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(opens in new window) H2020-MSCA-IF-2015

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Coordinator

ABERYSTWYTH UNIVERSITY
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 183 454,80
Address
VISUALISATION CENTRE PENGLAIS
SY23 3BF Aberystwyth
United Kingdom

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Region
Wales West Wales and The Valleys South West Wales
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 183 454,80

Participants (1)

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