Big Data Methods Research Articles

Big Data Methods and Analytics for Text, Audio, Video, and Social Media Data Concepts: Methods and Analytics for Text, Audio, Video, and Social Media Data

This paper provides a unified description of big data by integrating ideas from academics and practitioners. The primary focus of the paper is the analytical methods applied to big data. The focus this study places on analytics for unstructured data—which accounts for 95% of big data—makes it particularly noteworthy. The massive volumes of data produced by various machine-driven automated systems, including statistical, text, audio, video, sensor, and biometric data, are collectively referred to as "big data." In this essay, we consider the dimensions of various types of large data while addressing their challenges, applications, and problems. In this section, we'll cover social media data analytics along with content-based, text, audio, and video analytics.

The Impact of Built Environment in Shanghai Neighborhoods on the Physical and Mental Health of Elderly Residents: Validation of a Chain Mediation Model Using Deep Learning and Big Data Methods

As urban aging issues intensify, the impact of the built environment in urban neighborhoods on the physical and mental health of elderly residents has garnered increasing attention. Previous studies have demonstrated that the built environment is related to various health outcomes; however, most empirical research typically focuses on the objective physical environment, lacking measurements of subjective environmental perceptions. This study, using 24 neighborhoods in Shanghai as case studies, employed deep learning, big data methods, and surveys to collect 462 valid questionnaires from elderly residents. Structural equation modeling was applied to explore the relationship between the built environment and the physical and mental health of elderly residents, incorporating respondents’ subjective perceptions, physical activity, and neighborhood relationships as chain mediation effects. The results indicate that although there is no direct relationship between the built environment and the mental health of elderly residents, the built environment positively impacts mental health through enhancing subjective.

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.

Expert consensus on the application of big data on rehabilitation medicine (2024 edition)

With the development and progress of technology, the transformation of biomedical models, and the application of digital technology, the field of rehabilitation medicine has also generated massive and diversified big data. However, the current level of utilization of rehabilitation medicine is relatively low. Empowering the development of rehabilitation medicine through big data is crucial. In order to better promote the standardized development of rehabilitation big data in China, the Rehabilitation Big Data Working Committee of the Chinese Rehabilitation Medical Association organized an expert plan on the concept, significance, and necessity of rehabilitation big data; Rehabilitation big data source; Priority areas for rehabilitation big data application research; Basic research methods for rehabilitation big data; The standards and principles for establishing a rehabilitation database; Consensus has been developed on six aspects of clinical application scenarios for rehabilitation big data, providing reference for healthcare workers and researchers related to rehabilitation big data.

텍스트마이닝을 활용한 사회적 경제 분야 국내 연구 동향 분석 : 사회적기업과 소셜벤처를 중심으로

Social enterprises and social ventures, which constitute the actors of the social economy, are assuming the role of the 'third sector' by addressing a multitude of social issues that are not addressed by public and private companies. In this context, it is meaningful to gain an understanding of the characteristics of the social economy and the areas of study that fall within its remit. Accordingly, this study examines the defining characteristics and current research trends of social enterprises and social ventures, two pivotal entities within the social economy. Furthermore, it employs big data methods to analyse extensive data sets pertaining to social enterprises and social ventures. The abstracts of academic research on social enterprises and social ventures were extracted from the Research Information Service (RISS). Subsequently, three analytical techniques were employed in sequence: 1) word frequency and TF-IDF analysis, 2) co-occurrence network analysis, and 3) LDA-based topic modelling analysis. The results of the word frequency and TF-IDF analysis indicate that the economic aspects of social enterprises and their analysis represent a significant research area. Additionally, it can be concluded that research on organisational characteristics, their impact, and the value of the social economy is actively being conducted. Secondly, the 'co-occurrence word network' analysis demonstrated that research pertaining to social enterprises constitutes a pivotal topic within this field. The data analysis outcomes are of considerable significance, with the economic aspect of social enterprises and the purpose of enterprises also representing major research topics. Thirdly, eight themes were extracted through a comprehensive consideration of the implications of LDA-based topic extraction. The results demonstrated that social enterprises and social ventures are active in a multitude of fields, with a particular focus on the role and importance of various aspects, including consumer relations, support for individuals with disabilities, cultural access, urban regeneration, investment and responsibility, and partnerships. This demonstrates that social economy enterprises are playing a role in addressing social issues. The value of this study lies in its ability to draw inferences from the aforementioned research trends in the social economy domain through the utilisation of text mining analytical techniques. This study aspires to contribute to the formulation of prospective research directions in the field of social economy, with a foundation in the analysed research trends.

Integrating Internet of Things and Big Data as an Approach for Enhancing the Quality of Life in Egyptian Cities

Abstract In modern times, the concept of quality of life (QoL) has been a focal point in numerous studies, offering solutions to challenges faced by residents in new cities worldwide, including Egypt. To ensure citizens enjoy a high quality of life, cities are increasingly leveraging innovative technologies to address various aspects such as the environment, physical health, mobility, social interaction, psychological well-being, and the economy. Among these technologies, the Internet of Things (IoT) plays a significant role in enhancing people’s lives. By utilizing IoT as an information technology tool, cities can tackle their unique challenges. Consequently, the IoT method and the information obtained through Big Data (BD) analysis will enhance cities sustainability, safety, and liveability for residents. This research aims to explore the integration of IoT and BD for enhancing the quality of life in Egyptian cities. A qualitative methodology is employed to achieve this goal. Initially, a comprehensive literature review is conducted to uncover the relationship between improving quality of life in Egyptian cities and the use of IoT and BD methods. Additionally, a case study of Busan city, which has successfully implemented several IoT technologies to enhance the well-being of its inhabitants, is presented and analyzed. The findings from both the literature review and the case study highlight the positive correlation between the adoption of IoT and BD technologies and the overall quality of life in cities, spanning dimensions such as transportation, economy, social aspects, and the environment.

Identification of Pc4 type geomagnetic pulsations using big data methods In matlab environment

Identification of Pc4 type geomagnetic pulsations using big data methods In matlab environment

Concept graph construction and applied research of agricultural remote sensing

ABSTRACT Remote sensing technology in the new era gradually need a new scientific paradigm driven by big data and knowledge. However, in the current research, the use of existing knowledge is slightly insufficient. As for the whole research process, there is a lack of guidance based on the field knowledge. In view of the above problems in the field of remote sensing in agriculture, it is of great significance to organize knowledge based on concept graph and develop knowledge-driven big data analysis theory and methods. This paper focuses on the construction and application of agricultural remote sensing concept graph. Firstly, it explores the core contents, research hotspots and development trends of the current agricultural remote sensing field through reference visualization and analysis methods such as keyword frequency statistics, keyword co-occurrence, word detection with stronger citation burst and so on. Then, this paper summarizes the comprehensive ontology of agricultural remote sensing based on the results of keyword clustering. Thus, the schema layer of concept graph is constructed from top to bottom. Under the guidance of the schema layer, the data layer of the concept graph is artificially filled from the bottom up with different triads stored in Neo4j. In terms of application, retrieval, query and recommendation of the proposed concept graph for agricultural remote sensing tasks is a typical application case. Based on the results of the recommendation, it could guide the process of information extraction such as cultivated land parcel delineation and crop type identification at the parcel scale. The study of concept graph helps to summarize and generalize the relevant knowledge in the field of agricultural remote sensing. Relevant research provide effective solutions for automated and intelligent agricultural information extraction and application.

Developing a Cost-Effective Surgical Scheduling System Applying Lean Thinking and Toyota's Methods for Surgery-Related Big Data for Improved Data Use in Hospitals: User-Centered Design Approach.

Surgical scheduling is pivotal in managing daily surgical sequences, impacting patient experience and hospital resources significantly. With operating rooms costing approximately US $36 per minute, efficient scheduling is vital. However, global practices in surgical scheduling vary, largely due to challenges in predicting individual surgeon times for diverse patient conditions. Inspired by the Toyota Production System's efficiency in addressing similar logistical challenges, we applied its principles as detailed in the book "Lean Thinking" by Womack and Jones, which identifies processes that do not meet customer needs as wasteful. This insight is critical in health care, where waste can compromise patient safety and medical quality. This study aims to use lean thinking and Toyota methods to develop a more efficient surgical scheduling system that better aligns with user needs without additional financial burdens. We implemented the 5 principles of the Toyota system: specifying value, identifying the value stream, enabling flow, establishing pull, and pursuing perfection. Value was defined in terms of meeting the customer's needs, which in this context involved developing a responsive and efficient scheduling system. Our approach included 2 subsystems: one handling presurgery patient data and another for intraoperative and postoperative data. We identified inefficiencies in the presurgery data subsystem and responded by creating a comprehensive value stream map of the surgical process. We developed 2 Excel (Microsoft Corporation) macros using Visual Basic for Applications. The first calculated average surgery times from intra- or postoperative historic data, while the second estimated surgery durations and generated concise, visually engaging scheduling reports from presurgery data. We assessed the effectiveness of the new system by comparing task completion times and user satisfaction between the old and new systems. The implementation of the revised scheduling system significantly reduced the overall scheduling time from 301 seconds to 261 seconds (P=.02), with significant time reductions in the revised process from 99 seconds to 62 seconds (P<.001). Despite these improvements, approximately 21% of nurses preferred the older system for its familiarity. The new system protects patient data privacy and streamlines schedule dissemination through a secure LINE group (LY Corp), ensuring seamless flow. The design of the system allows for real-time updates and has been effectively monitoring surgical durations daily for over 3 years. The "pull" principle was demonstrated when an unplanned software issue prompted immediate, user-led troubleshooting, enhancing system reliability. Continuous improvement efforts are ongoing, except for the preoperative patient confirmation step, which requires further enhancement to ensure optimal patient safety. Lean principles and Toyota's methods, combined with computer programming, can revitalize surgical scheduling processes. They offer effective solutions for surgical scheduling challenges and enable the creation of a novel surgical scheduling system without incurring additional costs.

The Arcus Experience: Bridging the Data Science Gap for Nurse Researchers.

For years, nurse researchers have been called upon to engage with "big data" in the electronic health record (EHR) by leading studies focusing on nurse-centric patient outcomes and providing clinical analysis of potential outcome indicators. However, the current gap in nurses' data science education and training poses a significant barrier. We aimed to evaluate the viability of conducting nurse-led, big-data research projects within a custom-designed computational laboratory and examine the support required by a team of researchers with little to no big-data experience. Four nurse-led research teams developed a research question reliant on existing EHR data. Each team was given its own virtual computational laboratory populated with raw data. A data science education team provided instruction in coding languages-primarily structured query language and R-and data science techniques to organize and analyze the data. Three research teams have completed studies, resulting in one manuscript currently undergoing peer review and two manuscripts in progress. The final team is performing data analysis. Five barriers and five facilitators to big-data projects were identified. As the data science learning curve is steep, organizations need to help bridge the gap between what is currently taught in doctoral nursing programs and what is required of clinical nurse researchers to successfully engage in big-data methods. In addition, clinical nurse researchers require protected research time and a data science infrastructure that supports novice efforts with education, mentorship, and computational laboratory resources.

The Big Data Thinking in Furniture Innovative Design Under the Environment Configuration of Modern Chinese Houses

"Big data" is a popular word at present, and its application in various industries has gradually become widespread. In the furniture industry, the application effect of big data in the market is more and more prominent, and the great value brought by big data to furniture enterprises is also more and more obvious. How to use big data to obtain the information advantage brought by big data is also an important problem faced by furniture enterprises. This paper introduces the application and application methods of big data in new furniture design, and discusses how furniture design can effectively use big data to seize market opportunities and achieve new business development. This paper discusses how to apply more extensive data thinking in furniture design, model development and subsequent design to improve product innovation design, so as to provide effective reference for the new development of furniture design. At the same time, we explored innovative furniture design in combination with reality, and found that using big data can effectively enhance the pertinence of furniture design and optimize user experience.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Secured intelligent transportation with privacy retention through blockchain framework

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture.

Entropy-Based Subsampling Methods for Big Data

Entropy-Based Subsampling Methods for Big Data

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.

A Review of Studies on the English Translation of Shanghanlun

This paper mainly uses the methods of big data and statistical analysis to explore the current research status of the English translation of Shanghanlun, finding that there are a total of 69 pieces of literatures related to the book in China in the past decade, including 41 researches on the translation strategies, 21 on the comparison of the translations, 3 on the translation history, 2 corpus-based researches on the translations and 2 researches on the dissemination of the translations. The first two types of researches are more popular, and the research contents are mostly have something in common. In addition, the translation objects that have been mainly studied are the translation of Luo Xiwen, the translation of Nigel Wiseman and the translation of Li Zhaoguo, only accounting for a small number of existing translations. By contrast, the three aspects of research on the translation history, corpus-based research and research on the dissemination of the translations need to be further studied.

Understanding the University Student Experience Through Big Data Analytics

Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of student comments is being undertaken. The first and simplest use of big data analytics is for the identification of high frequency keyword groups, which, without big data analytics, would be extremely time consuming. However, the lack of context surrounding keyword groups severely limited the ability to draw meaningful conclusions and highlighted the need for human intervention in the analysis process. Future work includes sentiment analysis. This initial work is an impetus for further exploration of big data analytics methods in qualitative contexts, especially in dynamic contexts where rapid data analysis can form a basis for timely interventions.

СПЕЦИФИКА ПРОИЗВОДСТВА И ИСПОЛЬЗОВАНИЯ БОЛЬШИХ ДАННЫХ В ГОСУДАРСТВЕННОМ УПРАВЛЕНИИ

Differences in the practice of Big Data (BD) usage between the private and public sectors are causing a problem of public administration access to advanced BD technologies and methods used in business. The high potential of BD is in demand by public organizations while private businesses hold these technologies and methods. The study of barriers to the dissemination of a practice of BD usage in public administration allows identification of possibilities and directions for BD production and use by public organizations. Russian and foreign cases of production and incorporation of BD in public administration for their analysis were selected from scientific and business sources. The barriers limiting work with BD in public administration are identified: a legal regulation of BD implementation, the structure and functioning of the IT infrastructure of public administration, requirements for the quality of data as content, and the scope and scale of socio-political development problems in public administration. Analysis of BD application in public administration has shown the ways of BD production, such as signal reception and date entry, as well as the necessity of using the IT infrastructure of business. Ways of implementing BD vary due to their multitasking in the public and private sectors. Since BD accumulated by public organizations is in demand by private businesses, providing a data access mechanism can be an approach to enhancing the digital economy at the local and state levels. The study demonstrates the high importance of finding the ways to develop and disseminate BD for public authorities.

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Mathematical Modeling and Multi-Objective Optimization Methods for Big Data in Higher Education

Analysis of cryptographic authentication and manipulation detection methods for big data

The emergence of Big Data has made it possible to obtain more information about the customer and competitive base, as well as about market trends. Therefore, the desire of criminals to gain unauthorized access to this data also increases in direct proportion. However, not enough attention is paid to security issues of Big Data systems and their creation does not take into account the information security component. The purpose of the article is to develop approaches to the cyber protection of Big Data, which are stored and transmitted by telecommunication communication channels when responding to a request, namely their integrity and authentication. The main problems when working with Big Data are analyzed. Based on the results of the analysis of Big Data protection problems, a cryptographic transformation of data indexes when they are stored in the database and when transmitted in the form of hash codes is proposed to increase the efficiency of search by user requests. The expediency of using crypto-code constructions on elliptic curves of various types of modifications to form a pseudo-random substrate in order to increase the crypto-resistance of transmitted messages is proven. When forming hash codes, it is advisable to use modified elliptical codes, and under stricter conditions, the level of cryptographic resistance of authentication codes can be increased due to hybrid crypto-code constructions. A pseudo-random substrate can be represented by varieties that must equally ensure the necessary transformations and preservation of universality properties by the UMAC algorithm.