data analysis

This paper considers the problem of developing specialized software designed to support small businesses. It substantiates the relevance of creating such systems; architecture has been offered; and the results of development have been given. For practical use, a specific subject area has been considered, which allows to clearly understand the purpose and outcome of the work. These materials can be used to obtain ready-made solutions during the development of a software package on this topic.

This paper is devoted to the principles of constructing a software system of the aggregated data formation. The main principles of constructing a software system of the aggregated data formation are considered and their comparative analysis are carried out. An alternative principle of constructing a software system is proposed. This principle of constructing allows to eliminate the problems of fast and reliable data processing, scaling, automation of the software system components, improve data quality and security.

The trends of the past decade in architecture of the central processing unit show a clear direction towards multi-core processors with the number of cores increasing every eighteen months according to the Moore’s law. The shift from fast single-core to slower multi-core CPUs poses a question of scalability for a vast class of computational algorithms including algorithm used for data analysis. This paper presents the research result of using state of the art parallelisation programming paradigms to scale data analysis processes based on Self-Organising Maps.

In this article we present an algorithm for visualising the clustering structure of the data model captured by dynamic interval self-organising map (DISOM). The developed visualisation algorithm employs the Self-Organising Map for placing DISOM elements on the 2D lattice in conjunction with U-Matrix algorithm for visualization of data clusters.