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Big Data Analytics and Mining: investigating and testing distributed formulations of data mining algorithms that are suitable for the MapReduce paradigm and for other distributed computing approaches

Department of Computer Science

Applications accepted all year round Self-Funded PhD Students Only

About the Project

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 (Apache Hadoop). However, one of the most difficult and interesting challenge is not about the size of the Data, rather it is about the insight 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 a fundamental aspect. The potential of Big Data is in our ability to provide solutions to business problems, to provide new business opportunities and to facilitate a data-driven discovery in Science. The project will investigate and test distributed formulations of data mining algorithm that are suitable for the MapReduce paradigm and for other distributed computing approaches. For enquiries please contact . Keywords: Big Data, Data Analytics and Mining, Parallel and Distributed Computing

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