Big data

In the context of critical challenges related to global warming and the necessity of reducing carbon footprint, the electric car sector is experiencing significant growth. This progress inevitably leads to the need for expansion and modernization of the charging station infrastructure. This article conducts a detailed analysis of how big data processing technologies can contribute to the optimization of this infrastructure’s use, the efficiency of charging stations, and the development of personalized services for electric vehicle users.

An analytical review of the development of Data Lakes and its application in various industries, as part of Big data concept solutions, was conducted. The available standard architectural solutions for the Data Lake organization are considered. Also, specialized areas that require different or additional aspects to solve the tasks, depending on the field of Data Lake use, are taken into account. For the proper organization of Data Lake, various data processing tools are used, including distributed data storage systems, semantic networks, and especially metadata.

The modern world is characterized by a growth in the amount of data generated and collected. “Big data” provides opportunities for improving life and efficiency in various spheres. Creating smart cities where technology enhances the quality of life and service efficiency is an important direction in the use of big data. However, the use of digitization should not only concern places with a high population density. The answer to the challenge of digitizing populated areas of small size but relatively high population density is the creation of an intelligent region.

This article analyses the characteristics and prospects of the global big data market, and identifies the sources of data leakage in the global big data market. A study of the peculiarities of the development of the big data market in Ukraine in the economic context before and during the war was conducted, which revealed the influence of international institutions in the fight against cyber-attacks in the context of disturbances.

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis.

Predictive maintenance is a widely applied maintenance program that requires extensive support of computer aided systems. The program uses specific procedures that are to be addressed when developing predictive maintenance software solutions. Despite the fact that software solutions for predictive maintenance were introduced almost at the same time as the program emerged, it still remains a very actual field for the application of computer aided systems.

An overview of the basic principles and approaches for extracting information and processing information from web pages has been conducted. A methodology for developing a client-server system based on a tool for automation of work in Selenium web browsers based on the analyzed information about data parsing has been created. A third-party API as a user interface to simplify and speed up system development has been used. User access without downloading additional software has been enabled. Data from web pages have been received and processed.

An overview of intelligent data processing methods in the systems of the Industrial Internet of Things is presented in this paper. A comparison of Big Data analysis methods in industrial systems with a significant load is provided. The methods of distributed machine learning for data processing are offered. A software model for data analysis of different volumes is developed in the work. The analysis of the basic approaches to the organization of machine learning is carried out: federal and undistributed.

Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method.