Multidimensional Data Visualization Research Articles

Geometric separability of mesoscale patterns in embedding representation and visualization of multidimensional data and complex networks

Geometric separability of mesoscale patterns in embedding representation and visualization of multidimensional data and complex networks

Multidimensional data visualization and synchronization for revealing hidden pandemic information

Visualization is integral to uncovering hidden information in data and providing users with intuitive feedback for decision-making. Data visualization is crucial for transforming complex data into actionable insights across various domains. In recent years, coronavirus disease vaccines have become increasingly available to much of the population. However, the CDC (Centers for Disease Control and Prevention) often fails to consider multidimensional coronavirus pandemic data from a side-by-side perspective, limiting the ability of medical professionals and individuals to compare and interact with comprehensive data visualizations. Effectively displaying coronavirus and vaccination data collected from multiple sources is essential for interpreting pandemic transmission patterns and vaccine efficiency. This paper presents a new platform for innovative data visualizations that offers users intuitive feedback and a complete data story. We designed algorithms to seamlessly combine multiple parameters, synchronize attributes, and dynamically visualize data over time on a single webpage. Instead of integrating all attributes into a single plot, which can be overwhelming due to space limitations and make it difficult to extract crucial information from overcrowded display components, we developed algorithms to classify, enhance, and group all parameters based on their relationships and similarities. Furthermore, a side-by-side visualization method was created to dynamically link all parameters in multiple images for data exploration, trend comparison, hidden information detection, and correspondence analysis. Our platform provides real-time performance, enabling healthcare professionals to make informed decisions, communicate findings effectively, and uncover patterns that might not be apparent in raw data. The proposed multidimensional data visualization algorithms have broad applications in general data exploration and revealing hidden information.

Enhance watershed algorithms using principal component analysis capabilities

Traditional image segmentation algorithms have many drawbacks, such as over-segmentation and image distortion due to reflected light. The Watershed algorithm is one of the most popular image segmentation algorithms. Over-segmentation errors caused by overlapping targets in the image, as well as noise and glare, must be removed. In this article, we apply image processing using the watershed algorithm and propose to improve the algorithm based on principal component analysis. PCA is a popular technique for analyzing large datasets with many advantages per observation. PCA improves data interpretability while maximizing information content, enabling visualization of multidimensional data by finding image component gradients in a new space called the principle component that is unaffected by noise and reflected light. In contrast, the components mainly containing noise will eliminate with negligible information. This paper introduces three primary steps. The process involves applying the watershed algorithm to the image in the first phase, using the proposed approach (applying the watershed algorithm and suggesting an improvement based on principal component analysis) to the image in the second step, and comparing the outcomes of the two previous processes. Test results show that the suggested technique can achieve accurate and durable target shapes.

Data avatars: A theory-guided design and assessment for multidimensional data visualization

We propose the design of a novel method for multidimensional data visualization, called data avatars. We rely on the design science approach and the dual-processing theory (System 1-System 2) to guide our design and test the data avatars method against two well-known data visualization methods. The test involves a lab experiment with 429 participants from the general public and uses industry data. Our results show that data avatars provide a viable method for visualizing multidimensional data in terms of user cognitive performance. Further, the results indicate the important role of perceived cognitive load and enjoyment in users’ preference for data visualization design.

Online Convenience Store System using Multidimensional Data Visualization

The explosion of e-commerce businesses has seen the growth number of the online businesses worldwide. Retailers nowadays are opting with e-commerce platform to manage and monitor the business activities which is considerably convenience such as visualizing the sales data. However, Serbaneka is still using traditional way to store the sales data such as in spreadsheet papers which lead to data loss or misplaced the sales record. The objective of this research is to study the current usage of e-commerce system among hostel students in UiTM Tapah. A preliminary survey was carried out by eight selected hostel students in order to generate a list of criteria based on students' previous experiences with e- commerce systems. The following objective is to develop a web- based convenience store system using multidimensional data visualization for Serbaneka UiTM Tapah. The System Development Life Cycle (SDLC) method is used in the development of Online Convenience Store System for Serbaneka UiTM Tapah. Besides, multidimensional data visualization is used as method in visualizing the sales data of Serbaneka UiTM Tapah. Other than that, this research is to evaluate the usability of the system using Post-Study System Usability Questionnaire (PSSUQ) approach. The PSSUQ evaluation is evaluated by three selected experts. The results of PSSUQ evaluation stated that the experts satisfied with the development of Online Convenience Store System using Multidimensional Data Visualization for Serbaneka UiTM Tapah. This is due to the system's ability to assist retailers Serbaneka UiTM Tapah in managing and monitoring the business activities.

Sustainability Reporting in Energy Companies—Is There a Link between Social Disclosures, the Experience and Market Value?

As a result of the dissemination of the sustainability concept, social disclosures have become an important area of non-financial reporting, and the energy sector is no exception. The purpose of our article is a multi-faceted evaluation of sustainability reports published by companies operating in the Polish energy sector, from the perspective of social disclosures. The study involved the Polish listed companies that made up the WIG-Energia index. The time scope of the study covers the 2017–2021 period. In total, 54 non-financial reports were analyzed. In the first place, a comparative analysis was carried out to assess the social disclosures made by the WIG-Energia companies against the background of the biggest and the most liquid (blue chip) WIG20 companies. All the applied tools: ESG rating, NFR_S index, and multidimensional data visualization, have confirmed that the energy companies year by year have been presenting larger and larger extents of social disclosures. At the same time, it was observed that the companies appearing for the first time in the WIG-Energia index showed a very small extent of disclosures, whereas the companies which figured in the index throughout the studied period presented a relatively large extent of disclosures, due to their experience in preparing sustainability reports. Next, using the Pearson correlation coefficient (r), we examined the relationship between the energy companies’ market values and the extent of their social disclosures. The results of the statistical analyses have validated the strong and very strong correlation between capitalization and extent of disclosures. It is therefore possible to state that companies with higher market values are characterized by larger extents of social disclosures.

A multimodal, comprehensive characterization of a cutaneous wound model in healthy volunteers.

Development of pharmacological interventions for wound treatment is challenging due to both poorly understood wound healing mechanisms and heterogeneous patient populations. A standardized and well-characterized wound healing model in healthy volunteers is needed to aid in-depth pharmacodynamic and efficacy assessments of novel compounds. The current study aims to objectively and comprehensively characterize skin punch biopsy-induced wounds in healthy volunteers with an integrated, multimodal test battery. Eighteen (18) healthy male and female volunteers received three biopsies on the lower back, which were left to heal without intervention. The wound healing process was characterized using a battery of multimodal, non-invasive methods as well as histology and qPCR analysis in re-excised skin punch biopsies. Biophysical and clinical imaging read-outs returned to baseline values in 28 days. Optical coherence tomography detected cutaneous differences throughout the wound healing progression. qPCR analysis showed involvement of proteins, quantified as mRNA fold increase, in one or more healing phases. All modalities used in the study were able to detect differences over time. Using multidimensional data visualization, we were able to create a distinction between wound healing phases. Clinical and histopathological scoring were concordant with non-invasive imaging read-outs. This well-characterized wound healing model in healthy volunteers will be a valuable tool for the standardized testing of novel wound healing treatments.

Visual Perception of Property Rights in 3D

Despite the already advanced work on the construction of jurisdictional 3D cadastre models in many parts of the world and the technical feasibility of building very detailed 3D models of cities, relatively few specialists have focused on the aspects of visualizing property rights in three dimensions. Therefore, to complement the analyses carried out so far in this area, this research aims to investigate the perception of the visualization of multidimensional real estate data using different visual variables and by different audiences. The main contribution of the conducted research to the development of 3D cadastre visualizations is to start a discussion on the differences in their perception among real estate professionals and those who have no experience in this area and may have to use multidimensional property data. The research was conducted using a questionnaire-based survey method with the computer-assisted web interview (CAWI) technique. The questionnaire was completed by students of a course related to real estate law (geodetic science) and those who do not have regular contact with it (environmental engineering, medicine, sports, mechanics, and management). As a result of the survey, it emerged that the group studying geodetic science performed better on average than students in other fields of study. Additionally, the conducted survey confirmed the existing knowledge of the perception of the visualization of property rights in three dimensions. According to it, visualizations of property rights in 3D should use color. The use of transparency helps in visualisations made in grayscale but interferes with more complex colorful objects.

HyperCube4x: Exploring and Analyzing Data in Virtual Reality Using HyperRelational and HyperAnalyzer

Hypercube is a novel viewport management and information visualization system proposal that introduces three applications (called WorkScenes), focusing on interaction, immersive reading, data exploration, analysis, and visualization concepts. After presenting the conceptual description, interaction metaphors, and the prototype in a previous publication, this article presents HyperRelational and HyperAnalyzer, the WorkScenes focused on multidimensional data exploration, analysis, and visualization. First, the manuscript explores previous work on Human-Computer Interaction-related disciplines, such as cognitive psychology, cognitive engineering, and neuroscience. Then, we introduce HyperRelational and HyperAnalyzer, focusing on their fundamental concepts 1) Geometrical visualization; 2) mapping relationships among information as spatial dimensions. Also, the Screenshots help illustrate the mentioned concepts. Finally, the “Results and Discussion” section demonstrates how these features integrate with the flow, presence, and immersion of Virtual Reality, fit Shneiderman’s visual-information-seeking mantra and solve some desktop metaphor-related issues. Additionally, we present test results conducted with 26 participants that show an acceptability rate of 74% amongst users and highlight their positive feedback/experience regarding HyperAnalyzer. On the other hand, the System Usability Scale (SUS) evaluation scored 60.6731. The score demonstrates that HyperAnalyser scored a little better than Microsoft Excel. Therefore, we conclude that the concepts presented here are viable, but it is still necessary to evolve usability to make HyperCube commercially viable.

Data integration and analysis for circadian medicine.

Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.

The Exploration of Visualization Technology in Sichuan Opera

Visualization techniques can visually present and analyze data. This paper explores the visual field in the culture of Sichuan opera. Firstly, it summarizes the data of Sichuan opera and analyzes the characteristics of the data. Then, it introduces five kinds of visualization techniques: text visualization, hierarchical data visualization, network data visualization, spatio-temporal data visualization and multidimensional data visualization, and analyzes their application in the cultural visualization design of Sichuan Opera. Finally, the paper summarizes and puts forward the prospect.

Research Progress of Tumor Big Data Visualization

Background: As the number of tumor cases significantly increases, so does the quantity of tumor data. The mining and application of large-scale data have promoted the development of tumor big data. Among them, the visualization methods of tumor big data can well show the key information in a large volume of data and facilitate the human brain to receive information. Therefore, tumor big data visualization methods are a key part of the development of tumor big data. Process: This paper first summarizes the connotation, sources, characteristics, and applications of tumor big data, and expounds the current research status of tumor big data visualization at home and abroad. Then, this paper focuses on four mainstream visualization presentation methods of tumor big data, namely the visualization of tumor spatiotemporal data, the visualization of tumor hierarchy and network data, the visualization of tumor text data, and the visualization of multidimensional tumor data, and gives specific application scenarios. After this, the paper introduces the advantages, disadvantages, and scope of the use of five data visualization websites and software that can be easily obtained by readers. Finally, this paper analyzes the problems existing in tumor big data visualization, summarizes the visualization methods, and proposes the future of tumor big data visualization.

Methodology of analysis and forecasting of cyber incidents based on the method of principal components

The article discusses the theoretical aspects of the method of principal components, its application to the analysis of data on cyber incidents and the construction of prognostic models. Particular attention is paid to experimental results, their analysis and discussion in order to improve the effectiveness of the cyber incident forecasting methodology. This article aims to improve tools and increase the level of cyber security by applying the latest methods of data analysis and prediction of events in cyberspace. The usefulness of the method of main components in the analysis of data on cyber incidents is based on the possibility of reducing the amount of information analysis and determining the most significant factors of cyber incidents. The advantage of the described method of analyzing the statistics of cyber incidents is that it can be applied regardless of the nature of the distribution of random values - indicators of incidents. Due to the main properties of the principal component method, it can be quite successfully used to forecast cyber incident statistics, while ensuring the smallest forecast error. The general model of the risk of cyber incidents comprehensively takes into account the impact on cyber security of the entire spectrum of technical, organizational and human factors and is built on the basis of the scheme of the occurrence of a cyber incident, in which each incident is associated with a prerequisite for its occurrence. The specified approach allows for the analysis of direct cause-and-effect relationships that take place in the incident process and to identify both the main and hidden causes and types of events that lead to cyber incidents on the basis of statistical data. The example given in the article demonstrates the applied orientation of component analysis, in particular for the tasks of forecasting the number of initial indicators of cyber incidents based on a relatively small number of auxiliary (latent) variables expressing the causes of this phenomenon, visualization of multidimensional data and selection of typological features of cyber incidents.

Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School

Today's technology has improved to the point that it can be utilized to execute many activities in daily life with minimum effort, and the world has acknowledged the worth of education in one's life. The schools have to analyze student performance manually, which requires a lot of time and effort from teachers to work on. However, the increasing amount of student data becomes difficult to analyze using traditional statistical techniques and database data management tools. The objective of this project is to study the current problems in the online student performance system. A preliminary survey of 30 respondents was conducted in order to gather information based on previous user experiences with the online student performance system. The next objective is to develop an Online Student Performance System integrating Multidimensional Data Visualization and Chatbot for Primary School using Web Development Life Cycle that can visualize student performance systems to assist teachers and parents. Following that, this project employed a tool based on Multidimensional Data Visualization techniques. Google Charts and Dialogflow were used in this project to visualize the dashboard and construct a chatbot for the system. The last objective is to evaluate the usability of the system. There are three experts to test the project usability using the Post-Study System Usability Questionnaire (PSSUQ). The findings of the project can be used as a guideline to improve the system in the future. Overall, this project will assist teachers and parents in obtaining information about their students’ academic performance. The data about the students' performance can be displayed in the dashboard as a chart, graph, or diagram, and they can also communicate with the chatbot if they require assistance or guidance in using the system and obtaining their students' performance.

Design of Improved 3D Radar charts for Multidimensional Data Visualization

The rapid growth of Technology is leading to the exploration of data at a very deep level. This has caused data to increase exponentially at very high rates increasing the complexity of the relation between them. Multidimensional data thus come into existence and multidimensional data visualization tools help to understand the complexity of the relationship between data efficiently. Radar charts being very simple, easy, and more understandable due to their ability to reduce the complexity of the relationship between data plays a very important role in visualization. But radar charts still contain a few limitations that are being analyzed during this work. Radar charts are significant in many applications thus the work proposes a design for an improved 3D version of the Radar chart that can solve the problem in existing radar charts and achieve benchmarking.

A Novel Metaphor Graph Drawing Method for Multidimensional Data Visualisation and Its Case Study on COVID-19 Vaccination Analysis

Visualisation techniques have been one of the best data processing and analysis methods in recent decades, and they have assisted in data understanding efforts in various fields. Visualisation techniques for low-dimensional data are well developed and applied in multiple sectors; however, multidimensional data visualisation techniques still present some limitations, such as inaccurate data comparison and perception, exaggerated visual differences, label occlusion, and overlap. This study addresses the pros and cons and proposes a novel graphical drawing method, the multidimensional rose chart. It adopts the design idea of the Nightingale rose chart, but overcomes relevant limitations. The main challenges of this area include the incomplete presentation of multidimensional data, the neglect of the linkage of multiple attributes, the inefficient use of space, and the lack of simplicity of the interface. Contributions include enriching the representations of multidimensional data through the use of colour shades, area, and height sizes to represent values; straightforward data attribute comparisons via graph nesting; and detailed attributes showing the use of specific value labels. To verify the preliminary validity of this method, we imported COVID-19 data into experiments and further compared the final layouts with traditional methods, such as the line chart, bar chart, tree, parallel coordinate chart, and Nightingale rose chart, as well as their structures, functionalities, clear advantages, and disadvantages. The experimental results show that multidimensional rose diagrams perform effectively in presenting multidimensional data when comparing other graph drawing methods in our case, and the outcomes match existing works' conclusions in related COVID-19 research sectors. This work has the potential to provide a suitable supplemental approach to the multidimensional data analysis.

Analysis of Graphic Language Expression in Visual Communication Design

With the continuous development of society, only relying on words can no longer satisfy the mutual communication between people and countries. In the background of the data era, graphics language also meets more opportunities and challenges, so it is of great significance to transform data into the graphics language. This paper mainly designs a set of a data-based intelligent system. Through the research of multi-dimensional data visualization, firstly, the high-dimensional data is processed to reduce the dimension, so as to get the lower dimensional data, and then the low-dimensional data is transformed into a more intuitive type of graphic markers, so that users can understand the high-dimensional data more intuitively. The research results show that the system can provide users with the most appropriate label type and save users more time and energy. Data visualization has different experiences for different users, so it is a very important challenge to make users feel that pivoting is as effective as possible.

Methodology of application of methods of multidimensional and dynamic analysis when studying living standards of the population

The article presents fundamental approaches to the study of periods of development of socio-economic indicators and their mutual influence. The forms of influence of indicators on each other are investigated. The dynamic analysis of the standard of living of the population and the factors of social and economic spheres is completed with the tools of econometric modeling and canonical analysis. Birth rate, mortality, employment, unemployment, investments in fixed capital, GRP per capita, the account of resource production, the standard of living of the population and fixed assets according to the data of the Samara Region for the period 20062019, registered in the annual collections of state statistics bodies, are considered as indicators. The predicted values of the standard of living by various methods are calculated, confidence intervals for the studied indicators are constructed. By means of adaptive forecasting using the Brown model, forecast values are calculated and confidence intervals are constructed. Using the tools of canonical analysis, integral indicators are calculated and grouping by time factor is carried out. The spatial grouping of the time factor depending on the standard of living of the population and canonical integral factors is presented. According to the results of the analysis of autoregressive models, it was found that in terms of employment, unemployment, fertility, mortality, investment in fixed assets, GRP per capita, resource production and fixed assets, the impact of the indicator of the previous year is statistically significant, and in terms of the standard of living of the population statistically insignificant. In the second-order autoregression, it was found that all statistical indicators have an impact on the studied indicator, except for indicators of employment and the standard of living of the population. Thus, the forms of models of multiple linear regression, paired linear regression and autoregressive models allow us to assess the numerical impact of all indicators on the studied indicators, as well as their impact on the Standard of living of the population. Visualization of multidimensional data contributes to an in-depth analysis of indicators when grouping, for example, by the time factor.

Подход к оценке динамики изменения энергопотребления в регионе на основе использования нечеткой логики

Currently, energy efficiency indicators of regional infrastructure are presented as a rule in a quantitative form. However, decision makers need an assessment in a readable form to see the transition from a few quantitative values to a qualitative one that allows us in general to assess the dynamic patterns of energy consumption in the region. In this regard, the development of a method to assess the efficiency of energy consumption with linguistic recognition of the results obtained seems to be very relevant. The analysis is based on the retrospective values of indicators of the fuel and energy balances of the regions. The consolidated balance sheet includes about 500 indicator values for all used fuel and energy resources per year. To assess the multifactorial system under consideration, it is advisable to use the well-known matrix aggregation scheme, where the rows are the factors, and the columns are their qualitative levels. A standard five-level fuzzy 01-classifier is used as a scale for the transition from quantitative values of balance sheet indicators to qualitative values. A method is proposed to assess the dynamic pattern of energy consumption by means of linguistic recognition of a certain set of relative indicators of fuel and energy balances. Evaluation of the results in several regions using actual retrospective data has showed the relevance and reliability of the results obtained. At the same time, the results have been checked by methods of analysis and visualization of multidimensional data, as well as by experts. It has been established that the proposed approach to assess the efficiency of the use of fuel and energy resources can be used in the region policy of energy saving to improve the efficiency of decision-making.

5G network deployment and the associated energy consumption in the UK: A complex systems’ exploration

Investing in the communication infrastructure transition requires significant scientific consideration of challenges, prioritisation, risks and uncertainties. To address these challenges, a bottom-up approach was used to demonstrate the future of wireless network transmission and deployment. This study developed an agent-based model to explore the future deployment of non-standalone 5G networks, synthesizing multi-dimensional data visualization. In particular, this research took the UK as an example to investigate the spatiotemporal dynamic characteristics of 5G evolution, and further analysed the energy consumption and carbon footprint of 5G networks, as well as the consequent change in the operating expenses pattern. The simulation results show that 700 MHz and 26 GHz will play an important role in 5G deployment in the UK, which allow base stations to meet short-term and long-term data traffic demands respectively. Furthermore, due to the geopolitical restrictions and embargos, telecommunications may face additional costs of £0.63bn to £1.19bn when deploying 5G radio access networks. Network densification may cause some environmental and economic problems. Take a medium demand scenario as an example, it is found that the electricity consumed by the 5G radio access network will account for more than 2.1% of the total electricity generation, and indirectly lead to 990,404 tonnes carbon emissions in 2030.