Big Data Processing Technology Research Articles

The Analysis of Resilientnet-Realtime Disaster Response System

Responding to India's urgent need for effective disaster management, proposed framework ResilientNet, an innovative system leveraging real-time big data processing and advanced AI technologies. ResilientNet gathers diverse multimedia content from a wide range of social media services, including Twitter, Instagram, Facebook, etc., and utilises the GEMINI API, enabling comprehensive analysis and verification. Data is stored in the NEO4J database and visually represented on a user-friendly website dashboard for easy accessibility and insights. This research explores the efficacy of crowdsourced fact- checking, contributing to a novel disaster-focused tweet verification system. ResilientNet's amalgamation of crowdsourcing and AI creates a comprehensive graph of critical metrics and trends, enabling authorities to counter misinformation and direct disaster response efforts efficiently.

Research on Ship Data Analysis Platform Development and Its Application in Teaching

This paper delves into the significant importance of developing a ship data analysis platform, which aims to enhance the operational efficiency and safety of the shipping industry by integrating big data processing technologies. The development of the platform centers on crucial aspects such as data collection, preprocessing, analysis, and visualization, enabling real-time monitoring and intelligent prediction of ship operational status. This platform not only assists in optimizing route planning and reducing energy consumption costs but also provides robust support for navigational safety. In project-based teaching, this platform serves as a bridge between theory and practice, cultivating students' abilities in big data processing and analysis through case studies, practical exercises, and other forms, thereby promoting the cultivation of high-quality talents and technological innovation in the field of shipping.

Application of Real-Time Data Service Technology in Smart oilfield

Abstract Real time data services play an important role in the construction of Smart oilfields, which can help oilfield production managers better understand the production situation and make more accurate decisions. Real time data services based on the Data Lake platform will bring more efficient and safer production methods to oil and gas fields, promoting the upgrading and sustainable development of the Smart oilfield industry. The technical architecture of Smart oilfield real-time data service mainly includes data lake platform, big data processing, edge computing and other core technologies. The data lake platform provides infrastructure, platform services and application services for Smart oilfield. Big data processing technology is responsible for rapid and accurate analysis of massive data. edge computing refers to computing at the data source to reduce data transmission and improve computing efficiency. The key technologies include data collection, transmission, storage, analysis, etc. Among them, data collection mainly uses sensors, PLCs and other equipment to collect production data. Data transmission uses technologies such as the Internet of Things and industrial Ethernet to transmit data to the data lake platform. Data storage adopts distributed storage technology to ensure the safety and reliability of data. Data analysis uses machine learning, deep learning and other technologies to analyze data, Provide decision support.

APPLICATION OF THE THEORY OF PETRI NETS IN THE DEVELOPMENT OF SIMULATION MODELS OF BUSINESS PROCESSES BASED ON THE IDEF3 METHODOLOGY

In this study, we propose a model of an artificial neural network used as a specialized superstructure over a genetic algorithm, which allows influencing the process of finding solutions directly during the synthesis of solutions. Such a combination of methods will allow controlling the trajectory of the population in the solution space, which is especially important when working with big data processing technology, when stopping the solution search process due to the attenuation of the evolutionary procedure or finding the population in a local extremum requires stopping the genetic algorithm, performing additional adjustment of operators and restarting, the use of such an approach is ineffective, especially when working with big data and labor-intensive calculations. This article proposes a model of an artificial neural network that allows recognizing the state of the population of the genetic algorithm and making a decision to change the operating parameters of the genetic algorithm operators. The proposed model allows recognizing the processes of attenuation of the evolutionary procedure when solving the problem of structural and parametric synthesis of large discrete systems and determining measures of influence on the operating parameters of the genetic algorithm. This model recognizes the state of the population with an accuracy of more than 95%, which allows to significantly reduce the time for finding solutions in problems of applying a genetic algorithm to work with big data.

Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices.

With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-based memristors and their neuromorphic devices. We explain filamentary- and interface-type switching according to the type of conducting pathway occurring inside the active HP layer and the operating mechanisms depending on the species that make up this conducting pathway. We summarize the types and mechanisms of current changes beneficial for neuromorphic device applications and finally organize various suggested analysis tools and physical models to enable experimental determination of switching mechanisms from various perspectives.

Big Data Technology Usage in Electric Transportation Industry

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. Strategies for solving current problems, particularly in the areas of data security and standardization, are discussed, along with the impact of big data on the formation of new commercial models in the electric transport sector. Special attention is given to the analysis of existing scientific works and publications, which reveal a noticeable deficit of research focused on adapting big data technologies to specific regional conditions and analyzing the behavioral models of electric vehicle consumers. The article identifies key directions for future research aimed at exploring the potential of big data for the intelligent optimization of electric vehicle transport, particularly in areas such as demand forecasting, effective management of charging stations, development of new user services, and integration with broader urban transport management systems. Additionally, the article highlights challenges related to ensuring the confidentiality and security of collected data, the need to integrate diverse data and systems, and the current demand for qualified professionals in the field of big data analysis. The final section of the article focuses on the prospects of using big data in electric transport, their potential contribution to the development of smart city concepts, infrastructure improvement, and enhancing the quality of services for end consumers. Recommendations are provided for key stakeholders in the industry, with the aim of facilitating the adoption of strategic decisions that would take into account future opportunities and challenges. An analytical review of current literature sources and online publications is included, emphasizing the innovative nature of the research conducted.

Вопросы внедрения аналитических систем больших данных и других достижений цифровизации для повышения эффективности бизнеса горнодобывающих компаний

Currently, digitalization and widespread adoption of digital technologies are significantly changing people's activities in many areas. Digital technologies provide automation of business processes, data management, analytics, they support strategic decision-making and dictate the need to introduce new approaches to doing business in order to increase its efficiency and profitability, as well as to ensure sustainability of companies' development in modern conditions. It needs to be especially mentioned how the big data processing and analysis technologies and other Industry 4.0 achievements are introduced in the mining industry. The use of big data analytical systems in modern production, including the mining industry, provides an integrated approach to processing and analyzing a large amount of information. It also provides organizations with significant advantages reflected at various levels of management and strategic decision-making. The prospects for implementation and development of these digital solutions currently look very encouraging. Effective management of these processes provides companies with significant opportunities and advantages, allowing them to increase competitiveness, optimize the use of resources and increase the efficiency of their business as a whole.

LEVERAGING BIG DATA TECHNOLOGIES FOR ENHANCED PUBLIC PARTICIPATION IN PUBLIC FINANCIAL MANAGEMENT

The article is devoted to the topical issues regarding the implementation of Big Data technologies in public finance management. The application of Big Data has the potential to enhance transparency and accountability in the use of budgetary resources, increase trust in government, improve the efficiency of budget resource utilization, better understand citizens' needs, and engage the public in public finance management. The purpose of the study is to explore theoretical, methodological, and practical aspects, as well as to develop recommendations for the implementation of Big Data processing and analysis technologies to enhance public participation in public financial management. The article examines traditional methods of civil engagement in the budgetary process, identifies their disadvantages, and explores Big Data technology potential based on Computational Linguistics and Machine Learning to strengthen public participation. Developments in sentiment analysis and opinion mining have been adapted to the field of public finance. A generative model for analyzing public sentiment on social networks regarding public finance management has been constructed and tested. The approaches developed for using Big Data technologies can be implemented in the field of public finance to enhance public participation in their management as advisory tools for the realization of representative democracy and require further theoretical elaboration and practical application to improve the analysis of alternative sentiments, prevent manipulation of public opinion, and abuse within the network.

Research Trends Related on Hair Style in the Text Mining of Big Data Analysis

This study identified research trends by conducting keyword word frequency (TF), related word analysis (N-gram), and reverse document frequency (TF-IDF) through text mining, which analyzes text, which is unstructured data in big data, using the title and Korean abstract of domestic academic papers searched as hairstyles keywords in the Research Information Service (RISS). Through the research results, it was confirmed that hairstyles represent the characteristics of the times, and they are producing them or writing papers to understand preferences through statistics. The purpose of this study is to provide basic data to identify research trends through keywords extracted through text mining that analyzes text, which is unstructured data, in a new way with the development of big data processing technology.

DIGITALIZATION OF LABOR-LAW RELATIONS: BASIC PROVISIONS AND DEVELOPMENT PROSPECTS

The updated national goals of development of the Russian Federation for the period till 2030 and on perspective till 2036, approved by the Decree of the President of the Russian Federation dated 07.05.2024 No. 309, contain the most important directions of further development of our country, including such areas as: technological leadership, digital transformation of public and municipal administration, economy and social sphere involving automation of the most part of transactions within uniform sectoral digital platforms and data-based management model taking into account the accelerated implementation of big data processing technologies, machine learning and artificial intelligence. Achievement of these goals, defined by the Russian President as fundamental for the modern development of the state and systems of public administration bodies, is impossible without strategic development of the branch of labor law. The basis for achieving these national development goals is the development of a trinity of directions: digitalization of labor law relations - expansion of the use of artificial intelligence in the field of labor and employment - development of new forms of employment, such as remote work and platform employment. The goal of digitalization of labor relations is to increase efficiency, optimize resources, improve communication and ensure more flexible and transparent personnel management. As part of the digitalization of labor relations, companies apply various tools and technologies such as electronic HR management systems, online-platforms for recruiting and manpower planning, automated systems of control of working hours, virtual workspaces and other digital solutions. New digital solutions in HR management can become one of the drivers of development of the Russian economy, allowing for adequate savings for employers. The automation of production, the expansion of hiring remote workers, optimization of the use of artificial intelligence in work processes raise the question of deepening the scientific research of these aspects in labor law.

Personalized Learning Resource Push Evaluation Considering Image Big Data Processing Technology

Personalized Learning Resource Push Evaluation Considering Image Big Data Processing Technology

ANALYSIS OF DATA ORGANIZATION SYSTEM ARCHITECTURES OF THE SMART EUROPEAN REGION "CENTER OF EUROPE" BASED ON THE DATA LAKE TECHNOLOGICAL SOLUTION

In the contemporary landscape of digital transformation, the concept of a "smart region" emerges as a key strategy for regional development, utilizing data integration technologies to enhance the quality of life, optimize service delivery, and promote sustainable growth. The designation of the "Centre of Europe" as a smart region is justified by its strategic geographical location, encompassing Transcarpathia and adjacent areas of neighboring countries, coupled with a relatively low population density. This unique context creates compelling premises for rethinking traditional urban frameworks of smart cities on a broader scale, transforming them into a comprehensive smart region model tailored to the specific needs and opportunities of Central Europe. The rationale for implementing the "Centre of Europe" as a smart region stems from its advantageous position at the heart of the continent, which generates unprecedented opportunities for cross-border collaboration, innovation, and connectivity. The low population density of the region further highlights the potential for the adoption of advanced technological solutions capable of addressing a wide array of challenges and needs. By achieving the status of a smart region, the "Centre of Europe" aspires to become a leading example of how digital and technological innovations can be utilized to create an inclusive, efficient, and progressive regional ecosystem. Objective: The primary objective of this research is to conduct a comparative analysis of advanced data organization system architectures and their role in facilitating the transformation of the "Centre of Europe" into an intelligent European region. It also aims to explore the capabilities of utilizing data lakes for the effective management of large data sets and regional development, taking into account the unique geographical and demographic characteristics of the region. The subject of the study encompasses the architectures of big data organization systems in the smart region of the "Centre of Europe". The object of the study involves the processes of storing and processing large data within the smart region. Main characteristics: This article illuminates the main aspects of implementing big data processing technologies in the smart region of the "Centre of Europe", necessitating scalable and flexible solutions for large data storage, the use of efficient data management practices, and the integration of advanced technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence (AI) to enhance data processing and analytical capabilities. Furthermore, recommendations for regional authorities on adopting these technologies have been formulated, highlighting the importance of collaboration, support for investments in the region's digital infrastructure as key components for achieving the smart region vision. The construction of modern, high-tech data storage facilities using the Data Lake architecture enables the simultaneous use and processing of large volumes of data from various fields and sources. The authors have proposed conceptual models for forming data complexes from different functional areas of the smart region.

Fabrication of Sb2S3/Sb2Se3 heterostructure for potential resistive switching applications

The exponential growth of large data and the widespread adoption of the Internet of Things (IoT) have created significant challenges for traditional Von Neumann computers. These challenges include complex hardware, high energy consumption, and slow memory access time. Researchers are investigating novel materials and device architectures to address these issues by reducing energy consumption, improving performance, and enabling compact designs. A new study has successfully engineered a heterostructure that integrates Sb2Se3 and Sb2S3, resulting in improved electrical properties. This has generated significant interest in its potential applications in resistive switching. In this study, we have demonstrated the fabrication of a device based on Sb2S3/Sb2Se3 heterostructure that exhibits resistive switching behavior. The device has different resistance states that can be switched between high and low resistance levels when exposed to an external bias (−1 V to 0 V to 1 V). It also has good non-volatile memory characteristics, including low power consumption, high resistance ratio (∼102), and reliable endurance (∼103). The device enables faster data processing, reduces energy consumption, and streamlines hardware designs, contributing to computing advancements amidst modern challenges. This approach can revolutionize resistive switching devices, leading to more efficient computing solutions for big data processing and IoT technologies.

Efficient Real-Time Information Interaction and Discrimination: Exploration and Application of IT System Algorithms Based on Big Data Processing Technology

Efficient Real-Time Information Interaction and Discrimination: Exploration and Application of IT System Algorithms Based on Big Data Processing Technology

Integration of building information modeling and big data processing technologies

Integration of building information modeling and big data processing technologies

The Construction of Higher Vocational English Language Bank Based on Neurolinguistic Knowledge

Abstract With the advent of artificial intelligence and big data technologies, traditional approaches to teaching English in higher vocational education are increasingly inadequate for contemporary pedagogical demands. This study involved the collection and analysis of an extensive corpus of English learning materials facilitated by the use of neural network models to process this data. Consequently, we established an English language library tailored to the authentic language use and learning needs of higher vocational students. A substantial array of textual materials pertinent to higher vocational English instruction, including textbooks, online articles, and practical dialogues, was amassed using web crawler technology. These texts underwent natural language processing that included tokenization and the elimination of stop words. To develop a language model, we deployed a Transformer-based neural network for deep learning analysis of the processed data. Our findings indicate that the constructed language bank excels over traditional higher vocational English teaching resources in terms of vocabulary breadth, grammatical diversity, and practical utility. The language bank demonstrates notable benefits in analyzing vocabulary usage biases and enhancing students’ pragmatic language skills. This research substantiates the feasibility and effectiveness of constructing a language bank for higher vocational English utilizing neurolinguistic methodologies. The integration of advanced language modeling and big data processing technologies can significantly elevate the caliber of educational resources and instructional efficacy

Analysis of Big Data Storage and Processing Technologies in Cloud Computing Environment

Analysis of Big Data Storage and Processing Technologies in Cloud Computing Environment

A novel collaborative bearing fault diagnosis method based on multi-scale dynamic fusion network under speed fluctuating condition

Improving bearing fault diagnosis accuracy under speed fluctuation is a challenge in engineering applications. With the development of big data processing technology, a new solution, multi-sensor complementary information, has emerged. However, single-scale dimension compression, which is adopted in most multi-sensor data fusion methods, captures only a small amount of valuable information. To deal with this deficiency, a multi-scale dynamic fusion network (MSDFN) is proposed. First, considering the existence of non-stationary features in the fluctuating speed signal, the FReLU function is adopted to activate the features after considering contextual information. Then, multi-sensor features are fused by multiple scales to obtain richer feature information, and fusion features at different scales are weighted by using the attention mechanism. Finally, batch normalization is employed to standardize the variable speed feature distribution. The validity of the MSDFN is proved by conducting fault diagnosis experiments on two bearings under speed fluctuating conditions. Experimental results indicate that the MSDFN is not only effective in identifying various types of fault samples, but also shows higher stability in multiple trials when compared with other methods.

APPLICATION OF BIG DATA TECHNOLOGIES TO ASSESS THE NATURAL MOISTURE OF THE TERRITORY

Due to the sharp changes in climatic conditions in Western Siberia, the most pressing problem is food security associated with forecasting crop yields. There is a need to estimate the natural wetness of the area on the basis of agroclimatic indicators, among which the sum of active air temperatures and precipitation is currently the most widely used.
 Background. However, for a comprehensive assessment of the wetness of the territory, it is necessary to take into account the climate energy resources associated with evaporation of different time resolutions (day, decades, months, vegetation period, year). The initial meteorological parameters are described in the form of poorly structured information of a large volume. Therefore, various technologies and algorithms of machine processing are used in the work.
 Purpose. The application of big data technologies to assess the natural moisture of the territory
 Materials and methods. With the help of the high-level Python programming language and engineering libraries, a comprehensive assessment of the territory was carried out using the example of Mirny of the Kochenevsky District of the Novosibirsk Region in the context of long-term average data and two years 2019 and 2020. That the use of machine processing technologies related to NoSQL data requests, creation of data set and big data slices allows to store and process meteorological parameters using cloud services of different time resolution. This makes it possible to significantly reduce the time for a comprehensive assessment of the territory according to agroclimatic parameters. As a result of the work, precipitation distribution, temperature, relative air humidity, evaporability, humidification coefficients (Ivanov-Vysotsky and Selyaninov hydrothermal coefficient) were obtained.
 Results. The paper proposes to use technologies of big data processing using Python, including pre-processing of poorly structured hydrometeorological data, execution of NoSQL queries, compilation of summary reports on agroclimatic parameters. Pre-processing consists of processing hourly meteorological data, filling gaps in the data, making slices in big data to process multi-temporal queries (day, month, growing season, year). By the example of Mirny farm in Kochenevsky district of Novosibirsk region (Russian Federation), big data processing was performed to calculate agroclimatic parameters to assess the natural moisture content of the area and yield forecasting.
 Conclusion. The practical significance of the work is as follows:
 - the application of big data processing technologies has significantly reduced the time for the labor-intensive process of assessing agrometeorological parameters;
 - obtained aggregated meteorological parameters of different temporal resolution (hours, days, decades, months) allowed us to identify a strong variability of agroclimatic conditions for the territory of Mirny farm Kochenevsky district, located in the forest-steppe zone of Western Siberia;
 - perform an integral assessment of agroclimatic conditions by calculating the integral indices of moisture, climate continentality, and agroclimatic potential.

Optimization of ecological sports innovation teaching path based on multiple intelligence evaluation model

Abstract Rural sports tourism is an emerging industry that integrates rural tourism and sports tourism and plays an important role in broadening the field of sports and cultural consumption and driving rural economic development. Under the background of big data, how to better integrate rural sports culture and sports tourism becomes an important issue to improve the quality of rural tourism. This paper analyzes the big data of rural sports tourism on the Internet review platform based on big data processing technology, combined with the investigation and research on the current situation of rural sports tourism resources in a town. The big data analysis shows that rural sports tourists are only 7% more female than male, and the overall gender ratio is relatively balanced. In terms of age composition, the largest number of tourists were between 36 and 45 years old, accounting for 42.6%. In addition, rural sports tourists aged 26 to 60 accounted for 82.6% of all, indicating that rural sports tourism is dominated by middle-aged and elderly people. In terms of travel time, the largest number of rural sports tourists chose 2 to 3 days, accounting for 33.8%, and only 2.3% traveled for more than 7 days. In the choice of travel mode, 37.6% of tourists are organized by travel agencies, accounting for the largest number of travelers. From the big data emotional analysis of the evaluation words of sports tourists, the most frequent occurrence is characteristic, reaching 435 times, and the least frequent is danger, but also 89 times. Meanwhile, convenience and health also appear more frequently. Comprehensive analysis of the above, this paper believes that rural sports tourism should promote more local characteristics of sports culture and highlight differentiation while strengthening personal safety protection measures for tourists.