Big Data Visualization Research Articles

Review on Data Visualisation Techniques

The ascendancy of data visualization methodologies has rendered these instruments indispensable for contemporary decision-making processes within the epoch of exponential data proliferation. This review takes an in-depth look at various data visualisation techniques, providing an in-depth analysis of their applications, advantages and disadvantages. Data visualisation is an important tool for transforming large and complex datasets into intuitive graphical representations, enabling users to discover patterns, correlations and actionable insights more effectively. This review encompasses foundational visualization strategies including bar charts, line graphs, and scatter plots, in addition to sophisticated techniques such as heat maps, tree diagrams, and network diagrams, which are particularly efficacious for the representation of larger, more complex data sets. It also explores the growing importance of interactive visualisations, including dashboards and geospatial maps, which provide users with real-time data interaction and deeper exploration. In addition, emerging trends such as artificial intelligence, virtual reality and big data visualisation were seen as important advances that will shape the future of the field. Challenges such as data distortion, cognitive overload and accessibility issues were also analysed, highlighting the importance of choosing the appropriate technology based on the characteristics of the data, the user's task and the audience's needs to enable more effective communication and decision-making.

Spatial Data Infrastructure and Mobile Big Data for Urban Planning Based on the Example of Mikolajki Town in Poland

Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use.

Problem-driven visualization design of health and pollution big data

We present the design of a big data visualization application aimed at investigating the potential relationships between air pollution and perinatal health. We integrated data from singleton pregnancies in Brazil over a seven-year period and records of fine particulate matter (PM2.5) levels. Our methodology combines Design by Immersion and co-design, engaging specialists from various domains in problem-driven visualization design. The interactive visualization allows exploratory queries and comparative analyses based on medical birth certificate data restructured according to estimated conception dates. The collaboration of various domain experts was essential for leveraging complex data for an informed decision-making visualization.

Research on the Teaching Reform of “Big Data Analysis and Visualization” Course for College Students

Research on the Teaching Reform of “Big Data Analysis and Visualization” Course for College Students

Analysis and trend prediction of COVID-19 pandemic data based on big data visualization

Since the outbreak of COVID-19 at the end of 2019, this global public health crisis has profoundly impacted the socio-economic conditions and daily life of countries worldwide. To effectively combat the pandemic, scientists and public health experts rely on vast amounts of data to track the progression of the disease, evaluate the effectiveness of control measures, and predict future trends. Big data technology plays a crucial role in the analysis of pandemic data and trend forecasting. This paper will explore the methods of analyzing COVID-19 pandemic data and the application of trend forecasting.

Developing a smart forest protection mini program with Spring Boot: Integrating big data and image recognition technologies

Forests are essential for ecological stability yet pose significant management challenges due to their expansive and remote nature. Despite advancements in data acquisition, there is a lack of tools that effectively combine image recognition with big data analytics for real-time monitoring. This study introduces a "Smart Forest Protection" mini program, developed using Spring Boot, which integrates these technologies to facilitate immediate and efficient forest management. The program features intelligent plant recognition, user-driven incident reporting, waste recycling point displays, and a platform for disseminating forest conservation knowledge. Additionally, a big data visualization platform illustrates the spatiotemporal distribution of forests in Zhuhai, aiding governmental and environmental agencies in making informed decisions that enhance forest conservation and promote sustainable practices.

Global quantitative analysis and visualization of big data and medical devices based on bibliometrics

BackgroundIn the big data era, the healthcare sector grapples with increased data volumes and the push for more intelligent medical devices. This challenge is marked by data silos, higher data processing needs, and the quest for personalized medicine, emphasizing the importance of integrating and analyzing diverse big data from medical devices. This study employs bibliometric analysis to thoroughly analyze the research trends in big data and medical device research, aiming to identify key developments, patterns, and potential future directions in the field. MethodsThis study employed the Web of Science Core Collection, BIOSIS Citation Index, and Derwent Innovations Index for conducting keyword searches on ’big data’ and ’medical devices’, focusing on English-language articles, reviews, and patents, while excluding duplicates. Quantitative analyses and visualizations were facilitated using tools such as R 4.3.1, VOSviewer, CiteSpace, and Tableau. The research assessed the impact of journals and academic contributions through metrics like the impact factor and G-Index. ResultsOur analysis covered 592 articles and 795 patents. Notably, the annual growth rate of articles reached 62.19%, with the primary contributions originating from China, the United States, India, and England. Among the identified publications, “IEEE Access” emerged as the most prominent journal. The research identified key trends in the application of big data within medical devices, including the extensive use of artificial intelligence (AI), deep learning, Internet of Things (IoT) technologies, genomic data analysis, and bioinformatics. Case studies show how big data and medical device integration, exemplified by Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), and smart wearables, enhance diagnostic efficiency and care quality. The study further identified intensive care units, radiology departments, and oncology as key application departments leveraging this integration. ConclusionBig data and medical device integration have led to smart healthcare and personalized treatment, improving medical decision accuracy and inspiring new research directions. Future research efforts should explore the potential for incorporating even more expansive datasets and multilingual resources to further maximize the potential of this integration and advance medical technology in disease prevention, diagnosis, treatment, and management.

Investigation on big data evaluation and visualization of internet of things based on edge computing

With the rapid development and popularization of Internet of Things (IoT) technology, a large number of IoT devices have generated massive amounts of data. In order to efficiently process and analyze this data and extract valuable information from it, the analysis and visualization of Big Data (BD) of the IoT based on Edge Computing (EC) has become a hot topic in current research. How to efficiently process and analyze these data has become a current research hot topic. Based on EC technology, this paper used a solution for BD analysis and visualization of the IoT. This paper used Sobel operator and edge extraction algorithm to analyze the BD analysis and visualization research of the IoT based on EC. Firstly, useful information in the image is extracted through the Sobel operator to improve image quality, in order to better understand and utilize image data; Then, edge extraction algorithms are used to quickly and accurately extract the edge information of the image for subsequent data processing and analysis. This scheme used EC nodes to preliminary process and analyze the data, reduced the burden of the central server, and improved the response speed and real-time performance. At the same time, this article also designed a visualization platform to display the analysis results in the form of charts, making it easy for users to intuitively understand the data situation. The experimental results showed that the score of data and information visualization results based on EC was between 89 and 95. The research results showed that the IoT BD analysis and visualization research method based on EC could effectively improve the efficiency and accuracy of IoT data processing and achieve more intuitive data display through experiments. BD analysis and visualization technology can rely on IoT devices to extract useful information and knowledge, and help users better achieve monitoring, control, and decision support for IoT devices.

From static snapshots to dynamic panoramas: the evolution and future vision of palliative care atlas in cross-national perspectives

Palliative care is essential to global health services as it improves the quality of life of patients, their families and caregivers. The ATLANTES Global Observatory of Palliative Care (University of Navarra) was created a decade ago to promote a positive attitude towards patients with advanced illness in society and medicine. To do so, and over the past 15 years, ATLANTES has mapped palliative care data worldwide using public health, macro and comparative perspectives in different atlases. These have enabled data to be contextualised and good examples to be identified concisely and graphically. Atlases have been widely employed as advocacy tools within international institutions and ministries of health. While the aim and the perspective have remained unalterable over time, the methods and design have evolved throughout the publications from sober cartography and static infographics to big interactive data visualisation web tools. By embracing technology, ATLANTES has developed an open-access web mapping tool reuniting information from regional atlases, favouring global access to data. In 2022, matching the increasingly recognised need for robust monitoring of palliative care worldwide, ATLANTES became a WHO Collaborating Centre for the Global Monitoring of Palliative Care Development. This attempt to bridge the gap and ensure equitable care information in countries with limited palliative care access has resulted today in more accessible, self-explanatory, and visually appealing palliative care data.

Leveraging the Synergy of IPv6, Generative AI, and Web Engineering to Create a Big Data-driven Education Platform

The rapid advancement of network technology in China has significantly accelerated the implementation of information technology in higher education. Through the utilization of computer technology, multimedia technology, big data technology, artificial intelligence technology, and network communication technology, the integration of these technologies in university teaching has become widespread. This paper presents an analysis and discussion on the utilization of the latest IPv6 network transmission protocol technology to enhance the application of data collection in university education, with a specific focus on gathering information related to university faculties. By leveraging web engineering and multimedia technology as fundamental components, the network facilitates the sharing of educational resources among students, thereby enabling the reform of management approaches, fostering educational progress in China, and establishing a comprehensive big data-driven education platform specifically tailored to colleges and universities. Additionally, the incorporation of big data visualization and analysis tools allows for easy retrieval of existing university educational information, facilitates the creation of data charts, and expedites the utilization of data for its inherent value. Finally, the proposed approach employs generative AI to collect and analyze feedback from students and educators, followed by the application of web engineering techniques to continuously enhance the online education platform based on this feedback.

Visualization of the Signals Entropy Structure Based on Walsh–Hadamard Functions

New molecular genetic algorithms, as tools for the visualization and analysis of big data, have made it possible not only to illustrate the internal structure of DNA molecules within their parameters but also to explore the field of chaos theory, particularly to display processes and signals close to chaotic ones. This provides a new perspective on the problem of determining criteria for borderline states between order and chaos. This article demonstrates the differences between chaotic and quasi-chaotic signals when visualized with molecular genetic algorithms. It presents examples of molecular genetic mappings of signals generated using various pseudorandom noise generators, as well as acoustic signals. This article considers structural and integral (folded) mappings as one-dimensional and two-dimensional projections of the pattern. The authors illustrate the internal structure of the reconstructed signal mappings in spaces of fractional dimensionality, which is considered as a visualization of the entropy structure based on functional mappings in spaces of the fractional dimension. As a result of this research, it was found that the use of molecular genetic algorithms for visualizing information signals makes it possible to identify the so-called entropy structure of these signals. At the same time, the entropy structure of chaotic signals is absent.

How Popular is Your Name? Finding the Popularity of US Names Using Big Data Visualization

How Popular is Your Name? Finding the Popularity of US Names Using Big Data Visualization

Application of Big Data Visualization Technology in Quality Assessment of English Translation of Ancient Books

Application of Big Data Visualization Technology in Quality Assessment of English Translation of Ancient Books

Data Lake Management System based on Topic Modeling

In an environment full of competitiveness, data is a valuable asset for any company looking to grow. It represents a real competitive economic and strategic lever. The most reputable companies are not only concerned with collecting data from heterogeneous data sources, but also with analyzing and transforming these datasets into better decision-making. In this context, the data lake continues to be a powerful solution for storing large amounts of data and providing data analytics for decision support. In this paper, we examine the intelligent data lake management system that addresses the drawbacks of traditional business intelligence, which is no longer capable of handling data-driven demands. Data lakes are highly suitable for analyzing data from a variety of sources, particularly when data cleaning is time-consuming. However, ingesting heterogeneous data sources without any schema represents a major issue, and a data lake can easily turn into a data swamp. In this study, we implement the LDA topic model for managing the storage, processing, analysis, and visualization of big data. To assess the usefulness of our proposal, we evaluated its performance based on the topic coherence metric. The results of these experiments showed our approach to be more accurate on the tested datasets

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Intelligent big data visual analytics based on deep learning

Abstract In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network, and used the generative adversarial network to calculate the probability distribution of data. Then, the advantages of deep learning in intelligent big data visualization and analysis are explored from the dimensions of data preprocessing, dimension anchor layout, coordinate expansion and data analysis. Finally, the efficiency of the deep learning model is compared with that of other algorithms using indicators such as accuracy and recall, and the feasibility of this paper’s method is verified by empirical analysis using intelligent transportation data as an example. The results show that the model in this paper achieves an accuracy rate of 95.5%, the loss rate is stable at 0.2% to 0.4%, and the average running time is maintained at 20ms, which are all better than other models. The predicted and real values of traffic data for the Deep-STCL model using deep learning basically match, indicating that the deep learning model has obvious advantages in data visualization and analysis.

Review on audit data visualization method based on R language

To be competitive in today's market, companies must constantly assess and improve their operations, and financial data analysis has emerged as a crucial tool for this purpose. Executives can utilize data analysis to gain a deeper understanding of the underlying facts in their data and make better informed decisions about their company and the market. Multiple data models in the language make it possible to have a fully functional language environment with tools for statistical analysis and visual visualization of data. With its ability to enhance the quality of work in statistical computations and graphical analysis, the R language is ideally suited to the industrial data analysis environment. In this paper, we use a literature review methodology to examine the domestic and international literature on R language big data visualization auditing, analyze the visualization application areas of R language big data auditing, discuss the benefits and challenges of big data auditing, demonstrate how R language can be adapted to real-world auditing scenarios, and offer reasonable recommendations and optimistic outlooks for the field's future.

Analisa Visualisasi Data Covid – 19 Di Indonesia Menggunakan Tableau Big Data

This study explains the benefits of data analysis by visualization of Big data inoptimizing cases of the spread of COVID-19 in Indonesia. The data used is the COVID-19 Indonesia time series data from the Kaggle website. In this study, the author used tableau tools to analyze data based on worksheets of covid-19 distribution maps, COVID-19 statistics, details of active cases per province, death cases per province, active cases, death cases, confirmed cases and produced a dashboard of COVID-19 data. The results of the analysis obtained using visualizations in the form of graphs are very fast and optimize data processing so that they can find out which provinces are affected by active cases and deaths of COVID-19 from the highest to the lowest in Indonesia and can be used in decision and policy making for the Indonesian government.

Digital twins for building industrial metaverse

IntroductionThe concept of the metaverse, a virtual world where users can interact with a computer-generated environment, has received significant attention recently. ObjectivesThis study aims to investigate the application and fundamental technologies of Digital Twins (DT) in the development of the industrial metaverse, to enhance factory production efficiency. MethodsThe study adopts a literature review approach to explore the architecture and key technologies of the industrial metaverse, including DT, cloud rendering, virtual-real interaction, big data visualization, and the Internet of Things. Firstly, this study analyzes the architecture and key technologies of the Industrial Metaverse, which encompass DT, cloud rendering, virtual-reality interaction, big data visualization, and the Internet of Things (IoT). These technologies are essential components in enabling the transformation from traditional manufacturing paradigms to intelligent manufacturing within the industrial metaverse. Secondly, this study investigates the key technological aspects that support DT in the industrial metaverse. It highlights the crucial role played by DT in facilitating the advancement and realization of intelligent manufacturing. ResultsThe study finds that DT plays a crucial role in the industrial metaverse, enabling the creation of virtual replicas of physical assets that can be monitored and analyzed in real-time. ConclusionThe development of the industrial metaverse holds significant potential for the industrial sector, providing a platform for optimizing and improving manufacturing processes.

Construction of a Digital Shared Management Platform for Enterprise Finance in the Information Age

Abstract Based on the construction objectives of the enterprise financial sharing management platform, this paper focuses on the design concept and implementation process of each system module based on the overall system design under the framework of the financial sharing platform. The BP neural network algorithm is used to evaluate the financial information risk, the particle swarm optimization algorithm is used to optimize the database, the big data visualization technology is used for digitizing the financial information, and the metadata sharing technology is used to transfer the financial sharing data, to complete the financial information sharing. Through testing the platform, it is concluded that the real-time packet loss rate of financial sharing data is kept below 20%, and the average response time of multi-user clicks is within 2S. Therefore, the sharing function, the function of each module, and the performance of the platform in this paper can meet the needs of enterprises.