Big Data Processing Research Articles

The Interfaces Twitter Elections Dataset: Construction process and characteristics of big social data during the 2022 presidential elections in Brazil.

The main objective of this study is to describe the process of collecting data extracted from Twitter (X) during the Brazilian presidential elections in 2022, encompassing the post-election period and the event of the attack on the buildings of the executive, legislative, and judiciary branches in January 2023. The work of collecting data took one year. Additionally, the study provides an overview of the general characteristics of the dataset created from 282 million tweets, named "The Interfaces Twitter Elections Dataset" (ITED-Br), the third most extensive dataset of tweets with political purposes. The process of collecting and creating the database for this study went through three major stages, subdivided into several processes: (1) A preliminary analysis of the platform and its operation; (2) Contextual analysis, creation of the conceptual model, and definition of Keywords and (3) Implementation of the Data Collection Strategy. Python algorithms were developed to model each primary collection type. The "token farm" algorithm, was employed to iterate over available API keys. While Twitter is generally a "public" access platform and fits into big data standards, extracting valuable information is not trivial due to the volume, speed, and heterogeneity of data. This study concludes that acquiring informational value requires expertise not only in sociopolitical areas but also in computational and informational studies, highlighting the interdisciplinary nature of such research.

Digital Diplomacy in Russian International Studies: Research Methods

In the last decade, digitalization has had an increasing impact on the world political processes and is turning into an integral element of international life. Diplomatic practice, given the growing importance of the digital diplomacy, is no exception. In this context, the need for continuous improvement of the methods of studying these processes and phenomena is all the more pressing. This paper examines the key methods used in Russian studies on digital diplomacy, and identifies their strengths and weaknesses. The first section of the article covers publications built on the discourse analysis of small texts on international policy or their excerpts. The author concludes that such an approach allows for a correct description of the communication strategies of various political players. However, it is more suitable for linguistic research, since it does not address the issues crucial for political scientists, namely those concerning the evaluation of the effectiveness of digital diplomacy tools. The second section considers the research works based on the content analysis. The author argues that although this method provides an opportunity to process a larger amount of data and to reduce the impact of subjective factors, it has its limitations, particularly, inability to take into account the visual component of publications and to place them within a wider information context. The third section focuses on the scholarly papers drawing on the most time-consuming and demanding method of Big Data (hashtags) processing. The author stresses that its application is fraught with a whole range of quite specific methodological and purely technical problems, in particular, screening out bots and other ways of traffic manipulation. Against this backdrop, the author advocates further improvement in the methods of studying digital diplomacy by fostering a culture of data analytics and making a more active use of developments from other disciplines (philology, sociology, cultural studies, etc.).

The Effect of False Predictions of Machine Learning on the Security of the Big Data Environment

The exchange of data between customers and organizations has become a major target for hackers who seek to illegally access this data, compromising the three main components of security information: confidentiality, integrity, and availability (CIA). Structured query language injection (SQLI) is one of the most common forms of cyberattack. However, most of the previous research has only looked at SQLI attacks that target web-based applications. There hasn't been much time to sort the kind of SQLI payload that the client sent into the vast amounts of data needed to create machine learning models. Additionally, there hasn't been a study that looks at the risks of machine learning models making mistakes and how they affect the three information security principles. To address this gap, this research aims to create a model that serves as an intermediate protective interface that is a link between the customer's layers and the database server to improve security during communication from SQLI attacks. Additionally, it shortens the time required to identify the client's request type. Finally, study the impact of false predictions of machine learning algorithms on CIA. The proposed method is to train a model using a logistics regression technique (LR) with the Spark ML library that works to process big data containing SQL payloads (harmful and benign). Comparing our proposed model with previous studies, the results obtained show that the proposed model achieved outstanding results, with an accuracy ratio of 98.10%, a precision ratio of 98.13%, a call ratio of 98.10%, and an F1 index of 98.10%. The results also showed that the time needed to detect and prevent such attacks was only 00.09 seconds.

An Artificial Intelligence Home Monitoring System That Uses CNN and LSTM and is Based on the Android Studio Development Platform

This paper developed an artificial intelligence home environment monitoring system by using the Android Studio development platform. A database was constructed within a server to store sensor data. The proposed system comprises multiple sensors, a message queueing telemetry transport (MQTT) communication protocol, cloud data storage and computation, and end device control. A mobile application was developed using MongoDB software, which is a file-oriented NoSQL database management system developed using C++. This system represents a new database for processing big sensor data. The k-nearest neighbor (KNN) algorithm was used to impute missing data. Node-RED development software was used within the server as a data-receiving, storage, and computing environment that is convenient to manage and maintain. Data on indoor temperature, humidity, and carbon dioxide concentrations are transmitted to a mobile phone application through the MQTT communication protocol for real-time display and monitoring. The system can control a fan or warning light through the mobile application to maintain ambient temperature inside the house and to warn users of emergencies. A long short-term memory (LSTM) model and a convolutional neural network (CNN) model were used to predict indoor temperature, humidity, and carbon dioxide concentrations. Average relative errors in the predicted values of humidity and carbon dioxide concentration were approximately 0.0415% and 0.134%, respectively, for data storage using the KNN algorithm. For indoor temperature prediction, the LSTM model had a mean absolute percentage error of 0.180% and a root-mean-squared error of 0.042 °C. The CNN–LSTM model had a mean absolute percentage error of 1.370% and a root-mean-squared error of 0.117 °C.

Application of Deep Learning in Public Network Security Management

With the rapid advancement of information technology, network security issues have become increasingly prominent, posing significant challenges in the field of public administration. Deep learning, as a cutting-edge technology in artificial intelligence, is emerging as a critical tool for enhancing network security management due to its exceptional performance in big data processing and pattern recognition. This paper reviews the basic concepts of deep learning and its current applications in network security management, discusses the design and implementation of deep learning-based network security management systems, and analyzes their effectiveness and evaluation through specific application cases. The results show that deep learning excels in areas such as intrusion detection, malware detection, network traffic analysis, and anomaly detection, significantly enhancing network security defenses. However, the application of deep learning in network security still faces challenges such as data privacy, model robustness, and computational resources. The paper concludes by proposing future development directions, providing theoretical support and practical references for further improving public network security management.

Evolutionary Bioinformatics with Veiled Biological Database for Health Care Operations

The tremendous growth of biological data processing systems in the realm of health care applications has made real-time information accessible to everyone with no processing lags. Bioinformatics is even integrated into most wireless technology applications to account for all physical characteristics. The planned model focuses on evolutionary bioinformatics for medical sensor applications in health care. The optimization scenario is executed by combining genetic and ant colony optimization methods (GACO). In the proposed technique, the design concerns are implemented with appropriate transmitting and receiving modules, and individual bits are framed for extra bioinformatics data processing components. a design that completely minimizes all errors in the big data processing stage. Such a design completely lowers the overall error in the huge data processing state since all channels can be accessed in accordance with the framed bits. Furthermore, the quality of service is maximized because all channels carrying bioinformatics data are kept at high quality bits, increasing utility rates. The experiments were conducted using five scenarios to evaluate the effectiveness of the proposed design. The findings indicate that the proposed technique can handle bioinformatics data for healthcare in real time with a service quality of 95%.

Analyzing perception of Over-The-Top (OTT) sports broadcasting focus on social media big data analytics.

The purpose of this study was to analyze Korean subscribers' perceptions of sports broadcasting on OTT platforms. To achieve this, data was collected from various sources between 2018 and 2022 using 'OTT sports broadcasting' as the keyword, employing a big data processing solution called 'Textom.' The results were as follows: First, text mining was conducted to analyze word frequencies and examine TF-IDF scores. The text mining analysis revealed that words such as "broadcasting," "sports," "service," and "game" were used most frequently. In contrast, the TF-IDF analysis showed high scores for words like "SPOTV," "broadcasting," "Tottenham," and "live broadcast." Second, degree centrality and CONCOR analyses were employed for semantic analysis. The results indicated that degree centrality produced findings similar to the rankings based on word frequency. However, differences were observed in the rankings between degree centrality and TF-IDF. Next, a CONCOR analysis was conducted to explore the cluster relationships among words associated with the perception of 'OTT sports broadcasting.' This analysis resulted in the formation of four clusters consisting of words with similar characteristics: 'OTT soccer broadcasting,' 'Sports OTT platform,' 'OTT platform operator,' and 'Overseas sports broadcasting.'

Enhancing Business Competitiveness through Accounting Digitalization

Introduction: Amid rapid changes in the external and internal environment of modern business, more and more companies are turning to the introduction of the latest digital technologies to improve their accounting systems. This helps to increase the efficiency and accuracy of processes, as well as reduce the cost of routine work. However, the use of modern digital tools in accounting also involves certain risks and limitations, as well as raises questions about the ability to use the data for further management decision-making. Given the proven relevance of the topic, the purpose of the study can be defined as identifying key digital tools for the development and improvement of accounting to ensure the competitiveness of modern business in the long term. Methods: To obtain the results, the author used general scientific methods of analysis, synthesis, systematisation, processing of statistical material, generalisation and specification, as well as a specific method of hierarchy analysis, which allowed to identify the most optimal areas of accounting optimisation for a modern enterprise. Results: The study identified the main digital tools that can be used in the field of accounting and their impact on the system of accounting procedures in the company. The possibilities of applying various digital tools were identified and it was found that they are necessary to increase competitiveness in the long term. Conclusions: By applying the hierarchy analysis method, it is determined that the most promising for the development of the accounting system may be the tools for processing big data and using digital twins.

Research Progress in the Field of MapReduce Framework Based on Serverless Computing

In recent years, big data technology has presented a rapid development trend with each passing day, and its application in all walks of life is becoming more and more extensive and in-depth. At the same time, traditional computing frameworks are gradually showing serious challenges in performance bottlenecks and scalability in the face of massive data processing requirements, making it difficult to meet the growing needs of data processing and highly concurrent access scenarios. Firstly, this paper introduces MapReduce framework and serverless platforms, and then introduces the MapReduce framework model based on serverless platforms from several parts of implementation, application scenarios and technical principles. This paper argues that the serverless-based MapReduce framework will provide a more efficient and flexible solution for big data processing and analyzing, which is of great significance in promoting the development of data-driven decision-making and intelligent applications. The main content of this paper will be introduced from the basic overview of MapReduce computing framework and serverless platform, the implementation of serverless-based MapReduce computing framework, technical principles and application scenarios.

Scalable and fault-tolerant algorithms for big data processing in distributed cloud architectures

Scalable and fault-tolerant algorithms for big data processing in distributed cloud architectures

Exploring Drug Repurposing for Interstitial Cystitis/Bladder Pain Syndrome: Defining Novel Therapeutic Targets.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a debilitating pain condition of unknown etiology. Effective therapies for this condition could not have been developed in the last century. Drug repurposing is a practical strategy for enhancing patient access to successful therapies. It is an approach for discovering novel applications for licensed or investigational pharmaceuticals that extend beyond the initial medical indication. This work aims to identify repurposable medications through bioinformatics to discover potential drugs or compounds that can reverse the IC/BPS disease signature. The analysis involved examining the differentially expressed genes in IC/BPS patients with two distinct disease phenotypes (Hunner's lesion disease, non-Hunner's lesion disease) and controls using the datasets GSE11783, GSE28242, and GSE57560. The goal was to assess the reversal of the disease signature on the L1000CDS2 and cMAP platforms. Twenty-one compounds were repurposed, consisting of 11 small molecules, 10 chemical compounds, 3 natural products, and 6 FDA-approved drugs, currently used for clinical indications such as cancer, myelofibrosis, and diabetes. Bioinformatics can be useful for identifying therapeutic agents for IC/BPS by accessing and processing big data on molecular and cellular levels. Prospective in vivo experiments must validate repurposed drugs. The expansion of large-scale genome sequencing, gene expression studies, and clinical data for IC/BPS will improve successful drug selection.

Using big data and neural network technologies in the creation of adaptive methodological materials for the development of teachers’ research skills

Adaptive learning including adaptive methodological literature plays an important role in improving the effectiveness of teachers’ professional skills development and formation of their research competencies. The article is devoted to the application of big data processing models and neural network technologies in the preparation of adaptive methodological literature for the formation of teachers’ research competencies during the period from 2010 to 2023. Based on the analysis of scientific publications, technical documentation, surveys of teachers and educational analytics, the key principles, methods, and tools of personalization of educational content are identified. Types of data and sources of information for customizing materials to meet the individual needs of research teachers are determined. Criteria and metrics for assessing the effectiveness of adaptive methodological literature are developed, and the prospects of its development on the basis of big data and neural network technologies are substantiated. The problems and limitations of the approaches under consideration are revealed, and recommendations for overcoming them are given. Theoretical significance of the article consists in enhancing scientific concepts of the methodology of adaptive educational content development on the basis of intellectual analysis of big data. The practical value lies in the potential for optimizing the processes of creating personalized methodological resources and developing an adaptive system of pedagogical staff professional development.

Cloud cost monitoring and forecasting: issues and challenges

Cloud platforms are revolutionizing big data processing, but they also present significant challenges, particularly in cost management. As organizations move from on-premises solutions to cloud-based infrastructures, they often face unexpected costs due to the increasing volumes of structured, semi-structured, and unstructured data they collect and consume. Overcoming this challenge requires a thorough understanding of cost drivers, effective financial management practices, and strategic planning. This article examines the factors that influence the cost of cloud-based data processing and explores financial management best practices that maximize return on investment. It emphasizes the importance of adopting cost optimization strategies tailored to the unique needs of organizations operating in different industries. In addition to financial considerations, the article examines how cloud platforms enable marketing transformation through campaign automation, enhanced analytics, and personalization of the customer experience. These capabilities not only improve operational efficiency, but also drive greater customer engagement and loyalty. The research evaluates the challenges and opportunities associated with cost optimization in cloud systems. It highlights types of forecasts, optimization processes, and monitoring methods as critical components of achieving financial efficiency. A comprehensive three-step approach to effective financial management is proposed, focusing on understanding, controlling, and optimizing costs. In addition, the article evaluates the tools and solutions offered by cloud service providers, providing actionable insights for organizations to gain better control over their expenses. By integrating perspectives from both the financial and marketing dimensions, the study demonstrates how cloud-based systems can serve as a cornerstone for business innovation and growth. It concludes with practical recommendations for leveraging cloud platforms to improve operational and marketing efficiency while maintaining cost transparency and predictability.

Modern approaches to the organization of the technological process of big data collection and preparation and related new professions in the IT industry: DevOps-engineer and data engineer

The article modern technological approaches to the organization of big data collection and preparation, as well as the distribution and use of applications with the help of the Docker platform, loading and launching a set of applications on the basis of the container approach to virtualization, are considered. The basics of big data processing task formulation and technical problems of technical platform scaling and data quality assurance are briefly outlined. On the example of deploying and using PostgreSQL database management system with management tools and Apache Superset analytics platform, a modern approach to organizing data acquisition, transformation and subsequent demonstration in the form of a dashboard is presented. Work with data is shown on the example of a real open data set processing cycle with demonstration of all the main stages — loading, cleaning, preliminary analysis and visualization. In the context of the considered technologies and approaches to workflow organization in the form of a pipeline, examples of DevOps engineer and data engineer work tasks are given, the necessity of their work as part of a team is emphasized. The technical solutions demonstrated in the article can also be used in the study of the topics “Databases”, “Infographics”, and “Data analysis” in the advanced informatics course of technological profile as an illustration of the complexity of the relevant processes and in the training of future informatics teachers.

Enhancing Big Data Processing Efficiency in AI-Based Healthcare Systems: A Comparative Analysis of Random Forest and Deep

This research focuses on optimizing the speed of Big Data processing using Artificial Intelligence (AI) in healthcare applications. The study integrates Random Forest (RF) and Deep Learning (DL) algorithms with cloud-based computing systems to improve data processing efficiency. The dataset includes both structured data, such as Electronic Health Records (EHR), and unstructured data, like medical images. The results show that RF performs better with structured data, achieving a lower Mean Squared Error (MSE) and higher R-squared (R²) than traditional methods. Meanwhile, DL achieves superior accuracy and Area Under the Curve (AUC) in processing unstructured data. By utilizing the distributed computing power of Spark on a cloud platform, the processing speed was significantly enhanced, as demonstrated by a statistically significant reduction in processing time (p < 0.05) observed through a t-test analysis comparing Spark-based computing with traditional methods. Despite these improvements, challenges such as data privacy and infrastructure costs remain. Despite these improvements, challenges such as data privacy and infrastructure costs remain. This research provides a robust framework for real-time healthcare data analysis, highlighting its potential to improve decision-making processes and patient outcomes in medical services.

Enhancing Big Data Processing Efficiency in AI-Based Healthcare Systems: A Comparative Analysis of Random Forest and Deep

This research focuses on optimizing the speed of Big Data processing using Artificial Intelligence (AI) in healthcare applications. The study integrates Random Forest (RF) and Deep Learning (DL) algorithms with cloud-based computing systems to improve data processing efficiency. The dataset includes both structured data, such as Electronic Health Records (EHR), and unstructured data, like medical images. The results show that RF performs better with structured data, achieving a lower Mean Squared Error (MSE) and higher R-squared (R²) compared to traditional methods. Meanwhile, DL achieves superior accuracy and Area Under the Curve (AUC) in processing unstructured data. By utilizing the distributed computing power of Spark on a cloud platform, the processing speed was significantly enhanced, as demonstrated by a statistically significant reduction in processing time (p < 0.05) observed through a t-test analysis comparing Spark-based computing with traditional methods. Despite these improvements, challenges such as data privacy and infrastructure costs remain. Despite these improvements, challenges such as data privacy and infrastructure costs remain. This research provides a robust framework for real-time healthcare data analysis, highlighting its potential to improve decision-making processes and patient outcomes in medical services.

SCALE FOR DETERMINING THE VIRTUAL MANAGEMENT LEVEL OF AN ENTERPRISE

The soaring rise of the information age, the postmodern management approach, the instantaneous state of the era, the speed, and the processing of big data with different methods, and the compulsory of being socially distanced under the conditions of the new normal have granted vital importance to virtualization. The study aims to propose a scale that will determine the virtual management levels of enterprises by transforming traditional management functions into virtual management functions with the contribution of four features: adaptability-flexibility, teamwork, communications-leadership, and strategic vision. In the study, which was conducted with the participation of 265 people working in 45 different sectors and approved by the ethics committee, 4 main features were confirmed by factor analysis. Confirmatory factor analysis was used to test the validity of the four-factor structure obtained by exploratory factor analysis.

Digital Footprint as a Source of Big Data in Education

The purpose of this study is to consider the prospects and problems of using big data in education.Materials and methods. The research methods include analysis, systematization and structuring of information in the field of big data application in education, as well as modeling and software implementation of a test model for processing big data by using the Apache Spark framework.Results. The article considers the key aspects of using big data in education. In particular, the sources of big data in the form of a digital footprint of learning, methods of analysis and areas of application of big data are considered. At the same time, the following sources of big data in education were identified: electronic educational environment and electronic library of the university; mobile applications for learning; university website; social networks and forums; feedback data, requests and surveys; personal data, including psychometric characteristics of students; data from scientific smart laboratories; data from video surveillance and access control systems; data on the career path and success of graduates. The use of big data in education includes the following points: personalization of e-learning, issuance of personalized recommendations; data analytics; assessment and feedback; predicting student success; monitoring the quality of education; creation of a learner model; development of curricula based on employer requests; development of new educational programs; emergence of new learning models; improvement of university management processes; improvement of the work of the admissions office; modernization of software and hardware teaching aids; optimization of the teaching staff. The following problems are considered as problems of using big data in education: protection of personal data, the need for new methodologies and technologies for analyzing big data, the need for significant modernization of the technical means available in the education system, the need for qualified personnel. The article also provides a test example of analyzing a log file (event log) of an e-course using Spark SQL big data processing technologies. The example shows the potential and practical applicability of big data processing technologies to the tasks of analyzing the digital footprint of learning.Conclusion. Big data in education can provide unique opportunities for analyzing and optimizing the educational process, helping to identify trends, predict student success and adapt educational programs to the individual needs of students. However, the use of big data in the educational sphere also implies certain risks and challenges related to ethical aspects, protection of personal data and the need for personnel modernization of the existing education system. For the successful integration of data analytics into educational practice, it is necessary to develop not only technical resources, but also the level of digital security and ethics in the use of personal data.

Entangled Narratives: Insights from Social and Computer Sciences on National Artificial Intelligence Infrastructures

Abstract How do countries narrate their values and priorities in artificial intelligence infrastructures in comparative national and global contexts? This paper analyzes the policies governing national and regional artificial intelligence infrastructures to advance an understanding of “entangled narratives” in global affairs. It does so by utilizing artificial intelligence techniques that assist with generalizability and model building without sacrificing granularity. In particular, the machine learning and natural language processing big data models used alongside some process-tracing demonstrate the ways artificial intelligence infrastructural plans diverge, cluster, and transform along several topical dimensions in comparative contexts. The paper's entangled narrative approach adds to international relations (IR) theorizing about infrastructural narratives and technological diffusion. We provide patterned and granular results at various levels, which challenge and refine existing theories that attribute differences in infrastructures and technological adoption to geopolitical competition and imitation, top-down or linear international diffusion effects, and differences in political systems.

Hybrid CNN-LSTM Model for Accurate Long-Term and Short-Term Temperature Prediction: A Case Study for Bingöl and Tunceli

Extreme and sudden weather events experienced with global warming and climate change reveal the importance of accurate air temperature prediction. For this reason, it can be used to optimize decision-making processes for a wide range of applications from health and agricultural planning to energy consumption strategies. Artificial intelligence methods are successfully applied in many application areas due to their flexibility and efficiency. Traditional weather forecasting models are inefficient in detecting sudden fluctuations and complex, irregular patterns in data. Artificial in-telligence methods overcome these limitations thanks to their ability to process big data and capture long-term temporal dependencies. In this study, the aim is to predict future temperature changes more accurately by capturing patterns in past data with the developed CNN-LSTM hybrid model. The developed hybrid model is compared in detail with RF, SVM, CNN, and LSTM. The compared models were tested using daily average temperature data between 1961-2024 and hourly temperature data between 2020-2024. Experiments have shown that CNN-LSTM outperforms the compared models with R2 value above 0.97 in all scenarios.