Big Data Tools Research Articles

The Socio-Economic Situation of a Population as a Determinant of Childhood Obesity: Answers from Big Data

Big data tools are currently a major tool for assessing population changes. There is a causal relationship between low economic levels and a higher prevalence of conditions associated with malnutrition and obesity. One of the causes of low income in different areas could be a higher unemployment rate and a confluence of people who are migrating for economic reasons. Objective: To assess in a child population the effect of the unemployment rate, average income and immigration rate as a possible effect of increasing the prevalence of malnutrition associated with childhood obesity. Material and methods: Data collected from computerized clinical history episodes, studying the variables of sex, age, weight, height, of a pediatric population (year 2022), comparing it with the average income of their residential district, unemployment rate and immigration rate. Use of big data methods for the study of variables. Using the Cole-Green LMS algorithm with penalized likelihood, implemented in the RefCurv 0.4.2 software (2020), which allows managing large amounts of data. The hyperparameters have been selected using the BIC (Bayesian Information Criterion). To calculate population deviations from the reference, the reference was taken as being above 1.5 standard deviations from the average according to age. Results: 66,975 computerised episodes of children under 16 years of age and a total of 1,205,000 variables studied. The data and comparative graphs between districts of the population studied are represented with respect to the variables analysed. There are significant differences, with an increase in the rate of overweight in those areas with lower economic income and higher unemployment and immigration rates. Big data technology allows for more efficient population studies, selecting populations most in need of health intervention, optimizing scarce health resources.

The Future of Accounting: Efficacy of Big Data on Accountant’s Functions in the Accounting Information Systems

Before the advent of big data, accounting operations were performed mainly using historical and structured data. The emergence, however, introduced the use of big data to enhance and complement functions in the accounting information system, which improves the real-time and effective management of accounting data for varying uses of stakeholders. This study examined the efficacy of big data on accountant’s functions in the accounting information system in Nigeria. The study utilised survey research design method by administering structured Likert scaled questionnaire. This study was rooted in resource-based view theory and agency theory. The targeted population and the sample size was 283, which comprised accountants with experience in information systems. A purposive sampling technique was adopted to determine the respondent. The collected data were analysed using descriptive statistics and regression analysis. The empirical findings revealed that big data proxied with prescriptive analytics, predictive analytics, machine learning, and the Hadoop ecosystem had significant but negative effect on accountant’s functions in accounting information systems in Nigeria. This suggests that although these technologies are relevant and influential, their integration may be challenging or counterproductive to accountants' traditional roles. The negative effect could be due to factors like complexity, the need for new skill sets, inefficiencies in system adaptation, or resistance to change, which may hinder the effectiveness of accountants in fully leveraging these technologies within their work processes. This study concluded that while big data has the potential to significantly enhance accounting information systems, its integration into the accounting profession in Nigeria has been problematic. It was recommended that organizations should invest in comprehensive training programs for accountants, focusing on developing the necessary skills to handle big data tools effectively.

Leveraging Technological Research Methodologies to Improve Employee Performance in Organizations

The effective management of human resources in organizations is imperative for attaining optimum performance and eventual profitability. The increasingly competitive marketplace has compelled organizations to work towards attaining operational excellence, which heavily relies on the performance of its human resources. The adoption of cutting-edge research techniques has emerged as a pivotal strategy as organizations endeavour to achieve operational excellence. However, human resource managers have had to grapple with various challenges associated with the management of employee data generated and managed through digital platforms and tools, including issues such as data safety and security. This paper explores the intersection between tech-enhanced, advanced research methodologies and employee performance that is geared toward sustainable economic growth at the organizational level. Notably, the paper examines big data analytics as a tech-enhanced research tool leveraged by organizations in a bid to understand their human resource and the best way they can be managed to ensure high performance, which translates into high productivity and competitiveness in their respective sector or industries. In particular, this paper conducts a comprehensive review of recent extant literature regarding the adoption of big data analytics, which has revolutionized how organizations collect and analyze vast amounts of data, thereby enhancing their decision-making process. The reviewed literature focused on human resource aspects such as recruitment and selection; employee development; employee performance management and compensation structures. The review revealed that big data has emerged as a critical tool for managing large volumes of data that are acquired via numerous digital tools as organizations grapple with the integration of technology into their human resource practices. The technologies have helped human resource managers with these structured and unstructured data, they gain insight that in turn, guides their decision-making processes and improves the overall operational efficiency. The review outlines the transformative potential of integrating big data tools with human resource management and recommends that organizations should invest in these tools to enhance their recruitment and selection, employee development, performance measurement and upgrade their compensation structure.

Security issues analysis based on big data in cloud computing

This study will investigate the security problems that are linked with the Hadoop ecosystem, Big Data, Map Reduce, and cloud computing. The objective of this research is to analyze these concerns. Specifically, the primary emphasis is placed on the security concerns that are linked with big data in cloud computing. Big data applications have a lot to offer organizations, companies, and a broad range of industries, no matter how big or little. There have been issues with Hadoop and cloud computing security, therefore we also look at several possible solutions to these difficulties. The field of cloud computing security is experiencing fast growth in several areas, including computer security, network security, information security, and data privacy. Data, apps, and the infrastructure related to them are greatly helped by cloud computing's use of rules, technologies, controls, and big data tools to keep everything safe. Researchers also anticipate that cloud computing, big data, and their associated benefits will represent the most exciting new areas of study.

Association between autoimmune diseases and Alzheimer’s disease: Analysis using big data tools

ObjectiveThe objective is to analyze the prevalence of Alzheimer's disease in patients with and without a diagnosis of different autoimmune diseases and the possible association between both pathologies. Patients and methodsA multicenter, retrospective, cohort study was conducted to study the prevalence of Alzheimer’s disease among patients diagnosed with various autoimmune diseases compared to the general population. Data from electronic medical records from the Castilla-La Mancha healthcare system were analyzed using Natural Language Processing through the Savana Manager® artificial intelligence clinical platform. A total of 1,028,356 patients were analyzed, including 28,920 individuals with Alzheimer's disease and 999,436 control patients. ResultsOut of the 12 autoimmune diseases analyzed, 5 showed a significant association with Alzheimer's disease with p < 0.05. Myasthenia gravis had an increased prevalence of AD with OR 1.49 (95% CI 1.11–2), systemic lupus erythematosus with OR 2.42 (95% CI 2.02–2.88), rheumatoid arthritis with OR 1.38 (95% CI 1.24–1.54), polymyalgia rheumatica with OR 2.01 (95% CI 1.08–2.23), and pernicious anemia with OR 2.06 (95% CI 1.59–2.66). The remaining autoimmune diseases analyzed did not show a higher prevalence of Alzheimer's disease compared to the general population. ConclusionsThere may be an association between certain systemic autoimmune diseases and Alzheimer's disease. Further studies are needed to confirm our findings, establish causality, and explore the underlying mechanisms of this association.

390: Personalization of PTV margins based on SGRT and Big Data tools

390: Personalization of PTV margins based on SGRT and Big Data tools

Distribution-aware hybrid noise augmentation in graph contrastive learning for recommendation

The recommender systems are one of the most effective big data tools for solving the information overload problem, but data sparsity greatly affects its performance. However, most of the existing graph-based contrastive learning methods perturb the original graph structure for data augmentation, which may lead to semantic loss and subsequently degrade the recommendation performance. Meanwhile, studies have shown that improving the uniformity of data distribution is more effective than data augmentation using graph perturbation. In this paper, to enhance the uniformity of data distribution, we propose Distribution-Aware Hybrid Noise augmentation in graph contrastive learning for Recommendation (DAHNRec). Specifically, we design distribution-aware hybrid noise augmentation (DAHN) method to generate noise suitable for the users and items, instead of simply utilizing random noise following uniform distributions. By applying DAHN, the model can learn the appropriate noise distribution during the training process to enhance the uniformity of the embedding space. Additionally, we propose Balanced Bayesian Personalized Ranking (BBPR) as the loss function for recommendation tasks to improve difference characterizing between positive and negative samples. This improves the ranking tasks’ performance. Furthermore, we conduct extensive experiments based on public benchmark datasets. The results indicate that DAHNRec outperforms several existing methods. Furthermore, DAHNRec is a general recommendation model based on user–item interactions, which can be used to improve the recommendation performance in various real-world scenarios, such as e-commerce platforms and social media.

Implementation of Big Data Analysis Technology to Improve Decision Making in the Accounting Field

This article explores the Implementation of Big Data Analysis Technology to Enhance Decision Making in the Accounting Field. With the exponential growth of data, accounting professionals face challenges in processing and extracting meaningful insights. The study delves into the integration of advanced analytics tools within accounting practices to harness the potential of big data. The research begins by examining the current landscape of the accounting field and the traditional decision-making processes. It identifies the limitations and inefficiencies in handling vast amounts of financial data. Subsequently, the article elucidates the pivotal role of big data technologies in revolutionizing accounting practices. By employing sophisticated algorithms and data processing techniques, accountants can gain deeper insights into financial trends, risk factors, and performance metrics. Moreover, the study investigates real-world implementations of big data analytics in accounting firms. It highlights success stories and challenges faced during the transition, shedding light on the practical implications of adopting such technologies. The article emphasizes the importance of skill development among accounting professionals to effectively navigate and utilize big data tools. Furthermore, the impact of big data analysis on decision-making accuracy and efficiency is scrutinized. The findings underscore the potential for improved financial forecasting, fraud detection, and strategic planning. The integration of big data analytics is positioned as a catalyst for more informed and timely decision-making in the dynamic landscape of accounting. In conclusion, this research contributes to the evolving discourse on the intersection of big data technology and accounting. It provides valuable insights into the transformative potential of implementing advanced analytics tools, ultimately enhancing decision-making processes within the accounting field.

BANKING SUPERVISION THROUGH SUPTECH

Victor Ababii E-mail: ababii.victor@gmail.com PhD student, Academy of Economic Studies of Moldova Head of Division, Reporting and Statistics Department, National Bank of Moldova Chișinău, Republic of Moldova https://orcid.org/0009-0008-9573-0204 Abstract: The banking system requires continuous development to enhance stability and integrity of the financial sector by implementing the latest technological tools. Maintaining a sustainable financial system, resilient to the shocks generated by the digital age and able to adapt and prevent emerging cyber risks remains a priority for supervisory institutions, in response to financial supervision and risk management framework through leveraging state-of-the-art technology for better governance and control. SupTech's primary objective is to promote inclusion in the financial sector by focusing on governance management and digital financing. It`s goal is to support the development of an inclusive financial system built on a robust digital infrastructure that enhances efficiency, transparency, and resilience. As technological innovations advance, financial services have to evolve, requiring financial institutions to adapt their surveillance technologies (SupTech) to safeguard citizen interests. Establishing both normative and technical SupTech frameworks helps supervisory authorities to eliminate gaps in internal processes, streamline workflows, fortify risk management, protect consumers, and maintain financial system stability amid ongoing market changes. Implementing a SupTech framework involves several stages: diagnosing the institution's current state, defining the purpose, vision, and objectives of SupTech, identifying key drivers and adaptive capabilities, evaluating necessary actions, and detailing steps for successful implementation. A thorough diagnosis of the supervisory institution involves IT solutions that facilitate continuous bank monitoring and offsite supervision. This diagnostic analysis includes: (1) Data products – identifying the position and trends to reach the level of AI-augment business intelligence tools; (2) Analytics – aiming to implement analytics tools perspectives; (3) Access controls - with the position on the agency - wide access; (4) Storage – oriented towards the use target of big data tools; (5) Validation and processing – aiming at the use of advanced data processing; (6) Collection – using the latest AI-based collection trends or alternative data sources. Configuring the SupTech framework involves transforming vulnerabilities into opportunities. For example, automating data flows can address deficiencies in manual data handling, risk assessment, and report generation. Implementing a data warehouse can solve challenges related to data consolidation, multiple analysis tools, system integration complexity, and data correlation. SupTech aims to provide supervisory authorities with reliable, high-quality data, aligning with best practices in governance and data management. This ensures consistency in the supervisory process and resilience to technological advancements. Digitizing surveillance processes is crucial for operational efficiency, with advanced analytics and business intelligence solutions enhancing surveillance information and decision-making. Keywords: SupTech, Fintech, Regtech, risks, data model. JEL classification: G21

Корпусная интерпретация англоязычного экологического дискурса: лингвистические закономерности и функциональные особенности

English-language environmental speech has its own linguistic features. Ecological discourse is a relevant, self-sufficient, and highly identical communication continuum. This article introduces a corpus analysis of the English-language environmental discourse with its linguistic patterns and functional interpretation. The texts were considered not as static linguistic objects, but as a system of functional features in speech. The English-language research material was authentic and featured various environmental issues. Corpus analysis is a popular and reliable tool for big linguistic data while the corpus-discursive methodology proved to be an effective interdisciplinary tool. It was applied to the EuroNews Green Corpus, aggregated from 506 English-language texts on environmental issues. This innovative type of corpus can be qualified as instrumentally independent. The research proved the effectiveness of universal case managers or case shells of open architecture, e.g., Sketch Engine. Not only did it manage to collect the data, but also performed quite a complex structure analysis, interpreting, and discourse modeling. Corpus linguistics is looking for qualitatively new opportunities to identify complex linguistic relations. The case study provided a comprehensive description of environmental discourse as an object of linguistic research. Based on statistic details, the authors analyzed the categories of tokens, keywords, and terms to structure them as lemmas, collocations, and n-grams, which were presented as metadata. The metadata modeling provided concordances, thesauruses, and frequency glossaries. Environmental discourse had a pronounced lexical and semantic uniqueness resulting from specific ecological terminology. However, the multidimensional corpus analysis revealed an extremely low frequency of the key concept of ecology. Thus, this corpus study of the English-language ecological discourse demonstrated a good potential for further linguistic research.

Big data resolving using Apache Spark for load forecasting and demand response in smart grid: a case study of Low Carbon London Project

Using recent information and communication technologies for monitoring and management initiates a revolution in the smart grid. These technologies generate massive data that can only be processed using big data tools. This paper emphasizes the role of big data in resolving load forecasting, renewable energy sources integration, and demand response as significant aspects of smart grids. Meters data from the Low Carbon London Project is investigated as a case study. Because of the immense stream of meters' readings and exogenous data added to load forecasting models, addressing the problem is in the context of big data. Descriptive analytics are developed using Spark SQL to get insights regarding household energy consumption. Spark MLlib is utilized for predictive analytics by building scalable machine learning models accommodating meters' data streams. Multivariate polynomial regression and decision tree models are preferred here based on the big data point of view and the literature that ensures they are accurate and interpretable. The results confirmed the descriptive analytics and data visualization capabilities to provide valuable insights, guide the feature selection process, and enhance load forecasting models' accuracy. Accordingly, proper evaluation of demand response programs and integration of renewable energy resources is accomplished using achieved load forecasting results.

The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste and Optimize Patient Treatment.

Precision medicine is emerging as an integral component in delivering care in the health system leading to better diagnosis and optimizing the treatment of patients. This growth is due to the new technologies in the data science field that have led to the ability to model complex diseases. Precision medicine is based on genomics and omics facilities that provide information about molecular proteins and biomarkers that could lead to discoveries for the treatment of patients suffering from various diseases. However, the main problems related to precision medicine are the ability to analyze, interpret, and integrate data. Hence, there is a lack of smooth transition from conventional to precision medicine. Therefore, this work reviews the limitations and discusses the benefits of overcoming them if big data tools are utilized and merged with precision medicine. The results from this review indicate that most of the literature focuses on the challenges rather than providing flexible solutions to adapt big data to precision medicine. As a result, this paper adds to the literature by proposing potential technical, educational, and infrastructural solutions in big data for a better transition to precision medicine.

Особенности использования big data при исследовании грузопотоков на железнодорожном транспорте

Objective: to characterize the features and prospects of using big data tools and technologies in the management of the transportation process on railways. Methods: neural network modeling, system analysis, forecasting, programming, big data, predictive analytics. Results: a datalogical model of entities for storing up-to-date data on cargo flows is proposed, and a structure for building a system for accumulating information is proposed. In addition, the paper examines the applied issues of solving the problems of storing, receiving and processing data using big data methods. Practical significance: Improving the management of railway transportation processes in the context of digital transformation in terms of obtaining more accurate forecasts.

Digital Footprint: Assessing Student Satisfaction with Education Quality

The COVID-19 pandemic has changed the way learning is organized around the world. Russian universities have also been faced with the need to quickly transfer all teaching to an online format. The importance of student satisfaction with the education quality in online learning is increasing, since it is an important condition for motivation. The paper shows that based on the analysis of students’ messages in the social network, it is possible to observe and analyze the overall dynamics and trends in student community satisfaction with the quality of the learning / the efficiency of universities and conduct a comparative analysis of the identified characteristic data groups with their totality. It is shown that the data on the reaction of students of a particular university may have significant deviations from the totality of data, which reflects the characteristics of the reaction of students of a particular university to events occurring at the same time. This may indicate the internal differences of the university, which form an appropriate response to external events.To understand the satisfaction of students in the transition to a new implementation format of the learning. The digital traces of students from the social network VKontakte were analyzed using individual Big Data tools on the PolyAnalyst software platform. This made it possible to trace changes in the mood of students and, on the example of a single university, to identify and explain deviations in the attitude of students to the implementation of the learning, as well as to verify the methodology. The methodology developed by authors makes it possible to detect problematic issues in the university, including the moment of their occurrence, relevance, degree of concern of students. Such content analysis can be used not only to assess students’ satisfaction with the quality of the learning, but also to monitor the emergence of any problems that cause concern and strong reactions on the part of the student community, as well as other communities and individual groups.

Open Research Issues and Tools for Visualization and Big Data Analytics

Open Research Issues and Tools for Visualization and Big Data Analytics

Practices and barriers for big data projects

The adoption of big data analytics is increasing in every major industry, demanding investments in new projects, technologies, architectures, and processes to allow the integration of big data platforms with legacy systems; however, many organizations have failed to incorporate it effectively into their decision-making processes and project benefits have not been adequately captured. This study aims to further investigate how a big data analytics project can be implemented in insurance companies. A case study was conducted on one of the largest insurance companies in Brazil with interviews and document analysis. The study identified five main practices that were adopted to successfully implement a big data analytics project (implement automatic autoscaling alerts, use specialized big data tools, integrate the platform with legacy systems, comply with privacy legislation, and ensure the documentation of technical architecture using business process modeling), as well as four barriers that prevent its proper adoption (complexity of access to multicloud data sources, high processing requirements of unstructured data analysis, failure to attend to business necessities at the right time, and project delays brought by bureaucratic interdepartmental processes); some of these have not previously been identified. Finally, an action plan to address these issues is presented.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors.

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

LEVERAGING BIG DATA FOR PERSONALIZED MARKETING CAMPAIGNS: A REVIEW

The burgeoning field of big data analytics has revolutionized the landscape of marketing, offering unprecedented opportunities for personalized marketing campaigns. This review aims to synthesize the current state of knowledge on leveraging big data for personalized marketing, elucidating the objectives, methodologies, key findings, and conclusions drawn from recent research in this domain. The primary objective of this review is to explore how big data analytics can be effectively utilized to tailor marketing strategies to individual consumer preferences, behaviors, and patterns. Methodologically, the review adopts a comprehensive approach, examining a wide range of studies that employ various big data tools and techniques, including machine learning algorithms, data mining, and predictive analytics, in the context of personalized marketing. Key findings indicate that big data analytics significantly enhances the ability of marketers to understand and predict consumer behavior, leading to more effective targeting and segmentation strategies. The integration of big data has shown to improve customer engagement, satisfaction, and loyalty by delivering more relevant and timely marketing messages. However, challenges such as data privacy concerns, the need for advanced analytical skills, and the potential for data inaccuracies are also highlighted. In conclusion, while big data presents substantial opportunities for personalizing marketing campaigns, its effective implementation requires careful consideration of ethical implications, investment in technological infrastructure, and ongoing skill development. Future research directions include exploring the impact of emerging technologies like artificial intelligence and the Internet of Things (IoT) on personalized marketing, and developing frameworks for ethical data usage in marketing practices. This review underscores the transformative potential of big data in reshaping personalized marketing strategies, offering valuable insights for both practitioners and researchers in the field.&#x0D; Keywords: Big Data, Marketing Strategies, Consumer Behavior, Data Analytics, Personalized Marketing, Market Segmentation, Privacy Concerns, Ethical Challenges, Digital Transformation, Artificial Intelligence (AI).

Adopting artificial intelligence and big data tools across industry sectors in Morocco: an integrative literature review

Adopting artificial intelligence and big data tools across industry sectors in Morocco: an integrative literature review

An Overview of Big Data Challenges, Tools and Techniques

An Overview of Big Data Challenges, Tools and Techniques