Hardware Platform Architecture for Social Networks Analysis

Student: Balaeva Liubov

Educational Programme: Information Security Management (Master)

Year of Graduation: 2018

This work focuses on problem of a choice of hardware platform architecture for social networks analysis. The study of this problem includes a number of directions. The approaches that are used in analyzing the behavior of people (individuals and crowds) in the digital space, as well as the algorithm for analyzing social networks, including data collection, are considered. Particular attention is being focused on big data - methods for processing and storing bid data and large graphs are described. It also analyzes the main architectural problems of supercomputers in big data processing - in particular, the problems of the "memory wall", and possible technical solutions to these problems are formulated. As a result of the analysis, recommendations were made for choosing the hardware platform architecture for social networks analysis and commercial supercomputers conforming to these requirements with a description of their technical characteristics have been proposed.

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