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Big Data Analytics and Mining for Parallel and Distributed Computing Paradigms

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  • Full or part time
    Dr DiFatta
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Project Overview:

Big Data indicates very large and complex data sets that are difficult to process using traditional and sequential data processing applications. Data-intensive, parallel and distributed approaches are typically employed, such as the MapReduce programming paradigm (e.g., Apache Hadoop). However, one of the most interesting challenges is not about the storage and the management of the data, rather it is about the insights and the impact the analysis of the data can generate. From this perspective, providing effective and efficient algorithms and tools for Big Data Analytics and Mining is fundamental. The potential of Big Data is in our ability to provide solutions to business and to the scientific community which are based on the approach known as ‘data-driven discovery’. The project will investigate, develop and test distributed formulations of data mining algorithms that are suitable for parallel and distributed computing paradigms. Depending on ongoing collaborations, the project may contribute to multi-disciplinary applications for the analysis of very large data in one of the following domains: Climate Science, Neuroscience, or Finance.

Keywords: Big Data, Data Analytics, Data Mining, Parallel and Distributed Computing, Data Mining Applications

School of Systems Engineering, University of Reading:

The University of Reading is one of the UK’s 20 most research-intensive universities and is ranked in the world’s top 200 universities according to the 2013/14 Times Higher Education World University Rankings. Achievements include the Queen’s Award for Export Achievement (1989) and the Queen’s Anniversary Prize for Higher Education (1998, 2006 and 2009). The School of Systems Engineering has a strong reputation for its innovative research in computer science and information systems, cybernetics, and electronic engineering. Our research is highly-regarded nationally and internationally, with demonstrated real-world impact.

Eligibility:

Applicants should have a bachelors (at least 2.1 or equivalent) or masters degree in Computer Science or a strongly-related discipline. Strong programming and logic skills are preferable. Experience in Data Mining and Machine Learning are desirable

How to apply:

(1) Submit an application for a PhD in Computer Science using the link below.
(2) After submitting your application you will receive an email to confirm receipt; email should be forwarded along with a covering letter and full CV to Dr Giuseppe Di Fatta ([email protected]).
(3) In the online application system, there is a section for “Research proposal” and a box that says “If you have already been in contact with a potential supervisor, please tell us who” – in this box, please enter “Dr. Giuseppe Di Fatta”.

Application Deadline:

Applications accepted all year round.

Further enquiries:
Dr. Giuseppe Di Fatta, tel: +44(0)118 378 822, email: [email protected], http://www.personal.reading.ac.uk/~sis06gd/

Funding Notes

We welcome applications from self-funded students worldwide for this project.
Students from Brazil: we welcome and support applications for the Science Without Borders Scholarship (Ciência sem Fronteiras) - http://www.reading.ac.uk/sciencewithoutbordersscholarships.

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