data analysis

The increasing demand for precision agriculture has prompted the integration of advanced technologies to optimize agricultural practices. This article presents an approach to agricultural field data processing using a cloud-based data pipeline. The system leverages data from various sensors deployed in the fields to collect real-time information on key parameters such as soil moisture, temperature, humidity, etc. The collected data is transmitted to the cloud where it undergoes a series of data processing and analysis stages.

The article develops an intelligent system of analysis and neural network forecasting of battery charge consumption for automated vehicles (AGVs). For this purpose, the types of AGV and the methods of effective forecasting of their battery charge consumption were analyzed. It is established that they are based on optimal robot control processes; application of technologies to increase capacity and extend service life.

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.