Data Visualization Technology Research Articles

The Application and Exploration of Data Visualization Technology in Artistic Expression and Exhibition

Embarking on the transformation of virtual art museums, the confluence of data visualization technology and deep learning emerges as a catalyst for redefining artistic expression. Driven by the desire to change traditional approaches, this work utilizes a sophisticated deep learning model, the self-attention-based cycle-consistent generative adversarial network (SA-CCGAN), for transformative purposes. The objective of proposed Modernizing Virtual Art: The Collaboration of Data Visualization Technology and Self-Attention-Based Cycle-Consistent Generative Adversarial Network for Artistic Expression (MVA: DVT & SA-CCGAN) is to elevate artistic expression and exhibition practices within the virtual space, marking a paradigm shift in traditional approaches. SA-CCGAN, renowned for its ability to generate realistic and coherent artistic representations, serves as the cornerstone of this transformative endeavor. The workflow intricately incorporates SA-CCGAN into the virtual art museum context, enhancing stylized outcomes and capturing global geometric features. This yields a computationally efficient representation of high-dimensional visual data, revolutionizing digital art presentation. The fusion of data visualization technology, creativity, and meticulous design not only signals innovation but also has the potential to redefine the virtual art landscape, fostering accessibility, engagement, and a deeper cultural exchange in digital art exploration.

Research on pricing and stocking of fresh goods based on nonlinear programming

In the fresh supermarket, due to the particularity of fresh goods, the fresh-keeping time of vegetable goods is relatively short, generally the wholesale goods of the same day need to be sold on the same day, if the day is not sold, it will produce waste phenomenon. Therefore, fresh supermarkets generally choose to price and replenish goods every day according to their experience and sales status. By establishing the relevant mathematical model, this paper gives the replenishment and pricing decision of vegetable commodities after the supermarket optimization, so as to maximize the profit of the supermarket. First, the box chart is used to pre-process the data, and then the distribution law of the sales volume of each vegetable category and single product over time is analyzed by data analysis and visualization technology. Then the Pearson correlation coefficient of the sales volume of each vegetable category and each single product is obtained by heat map analysis, and the correlation relationship between them is inferred. Then, by calculating the average daily sales pricing of various vegetable varieties as the cost plus pricing of dishes, using linear function and logarithmic function to fit the sales volume and sales price of each vegetable category to get the functional relationship between the two. Finally, ARIMA model is used to predict the wholesale price of each dish within seven days, and then the equation model is established. Finally, SLSQP method is used to predict the total amount of replenishment and pricing strategy of six vegetable categories in the next week.

Application Analysis of Data Visualization in Tiktok E-commerce

With the rapid development of the e-commerce field, the application of data visualization technology is more extensive. Visual development in the field of Tiktok e-commerce can improve marketing accuracy and optimize consumers' shopping experience. However, visualization development in the field of Tiktok e-commerce has been affected. Based on this, the paper proposes to optimize Tiktok e-commerce by means of data optimization and integration. This paper uses the literature research method to analyze the application of data visualization in Tiktok e-commerce. Through reading a large number of literature, it is found that data visualization is of great help to the development of e-commerce. The technical realization and function summary of data visualization in Tiktok e-commerce are studied, but there are still some problems in commodity information, commodity quality, user positioning, logistics, and other aspects. Finally, some solutions are proposed.

Log specification and intelligent analysis method based on oil and gas pipeline SCADA system

Abstract As the control center of the natural gas long-distance pipeline network, the SCADA system shoulders the important tasks of data collection and monitoring of the whole long-distance pipeline, gas transmission management, production scheduling, operation and maintenance coordination in production, and plays a very important role in the whole oil and gas pipeline. In this paper, firstly, the SCADA system for localized long-distance pipelines is explained in detail, including its basic structure and special solutions to problems. Secondly, the AdaBoost algorithm, which combines the MapReduce parallel computing framework, is introduced to collect and process data from the operation logs of the SCADA system and normalize the logs. Finally, to test the interaction between the improved AdaBoost algorithm and the SCADA system, a system test was conducted. The results show that the average latency of scheduling the logs of the SCADA system by AdaBoost algorithm with MapReduce parallel computing framework is only 39.82ms, the average processing speed of the log normalization file data under the multi-threaded mode of the system reaches 86.51GB/s, and the effective accuracy of the fault diagnosis is as high as 90.36%. This shows that the oil and gas pipeline SCADA system interacting with data visualization technology can process operation logs more quickly and can carry out real-time intelligent supervision of the working status and operation parameters of the whole auxiliary system, promoting the intelligent development of the oil and gas pipeline SCADA system.

Study on the application of data visualization technology in human resource management and employee turnover prediction

Abstract This paper presents a method to process the dataset of departing employees. The clustering categories of separated employees are accurately determined by fuzzy c-mean clustering and improved clustering FCM algorithm, and new samples of separated employees are generated using SMOTE algorithm to reduce noisy data. The kernel function trick of SVM is used to achieve clustering oversampling, improving classification accuracy. For imbalanced data, this paper uses a new integrated learning algorithm for constructing evaluations, PIBoost, combined with a full-sample cost-aware weight algorithm to improve the generalization ability of the SVM classifier, which has better classification results for various data sets. In the model performance comparison, K-AFCM-SMOTE-SVM has the highest accuracy with a value of 0.89. In the ten-fold cross-validation accuracy comparison, the K-AFCM-SMOTE-SVM model has a better overall performance index than the other two, with an average cross-validation accuracy of 0.932.

Analysis of the Influence of Parents Educational Level on Students Test Scores Based on Decision Tree

Under the background of the double reduction policy promulgated in July 2021 and the Peoples Republic of China Family Education Promotion Law promulgated in January 2022, improving family education and promoting home-school cooperation have become the focus of Chinas education reform and the key to improve the quality of students. The dataset of this paper comes from the Kaggle website. By using data visualization technology and ID3 algorithm of decision tree, the influence of various indicators on the accuracy of the decision tree prediction model is compared, and the relationship between parents education level and students test scores is further explored. The final results show that the education level of parents has a certain correlation with the test scores of children, and the students whose parents have higher education level generally have better scores.

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.

The value of novel technologies in context to performance measurement and management: A systematic review and future research directions

The purpose of this paper is to systematically review research related to the value of new technologies in the context of performance measurement and management. The review synthesizes findings from previous studies to provide a comprehensive understanding of the current state-of-the-art of various novel technologies and how they assist performance measurement and management across various industries. Scopus was selected as the main electronic database for this study. As a principal result, we found that research related to the value of novel technologies in the context of performance measurement and management is formed around six research streams: automation techniques, predictive analytics, real-time data collection, visualization, collaboration tools, and blockchain technology. As a conclusion, the research presents that novel technologies can be used to track and monitor performance, automate processes, and streamline operations. The findings of this review will bring more coherent value to the current state of the literature and contribute to a diverse body of knowledge.

Research on the analysis of traditional music tunes in the context of network information technology

Abstract People’s traditional music passed down from generation to generation should be heard by the world, and music tunes, as one of the indispensable basic elements of traditional music, need to be studied more deeply and systematically. In this paper, based on modern network information technology, we use big data visualization technology to analyze and study traditional music tunes. Using the RGB color space principle to understand the visualization technology, the number of research literature related to traditional music tunes in the past 20 years is obtained through literature collation, and the chart way of visualization using Sankey diagrams is analyzed to conclude traditional music tunes research. The data show that most research literature on traditional music tunes features an average of 6 articles per year. The number of ephemeral articles has gradually increased over the decade, with the total effective number of articles being about 7 in 2002 and increasing to about 39 in 2022. The research institutions are mainly concentrated in many universities in Henan, Yunnan, Hunan, Guangdong, and Guangxi, but the main traditional music tunes studied in each place also Have differences. The research on traditional music tunes by universities has a guiding and leading role, and the increase and depth of related research literature are also conducive to the protection and inheritance of traditional music tunes and integration and innovation in each region.

Exploring the Application of Big Data Visualization Platform in Urban Traffic Data Analysis

With the rapid development of information technology and the popularity of the Internet, the era of big data has come. The urban traffic system continuously generates a large amount of traffic data, which are so large and diverse that the traditional data processing methods have been unable to effectively process and analyze them. Therefore, it is necessary to process and analyze urban traffic data with the help of emerging big data technologies and methods. Starting from introducing the characteristics of big data visualization platforms and common big data visualization tools and technologies, the article analyzes the scale and characteristics of urban traffic data and the limitations of traditional data analysis methods, discusses the advantages and application prospects of visualization platforms, and focuses on the four perspectives of data cleaning and pre-processing, traffic flow and congestion analysis, traffic behavior and trend analysis, traffic simulation, and planning support. The application of big data visualization platforms in urban traffic data analysis is discussed.

Application of clustering methods and data visualization for decision making in higher education

This paper aims to present a case study to demonstrate the practical application of data visualization techniques and machine learning algorithms. Examining the data using various algorithms enables us to make predictions about how data visualization affects decision-making. Our study’s results support the notion that data visualization influences decision-making. Moreover, we delve into the implications of employing data visualization technology in the academic community, including faculty and students. Furthermore, we evaluate how data visualization influences decision making in higher education institutions, showcasing its potential to enhance the efficiency and speed of decision-making for stakeholders.

Analysis Using R Software: A Big Opportunity for Epidemiology and Public Health Data Analysis

R is a programming language, open-source, developed by various of the world's most active statisticians with powerful function and visualization for data analysis from simple to complex data such as machine learning and artificial intelligence. Data visualization technologies have the ability to assist public health professionals with decision-making. Visualization appears to help decision making by increasing the quantity of information communicated and reducing the cognitive and intellectual strain of processing information. There are numerous commercially available statistical software packages that are widely utilized by epidemiologists worldwide. For industrialized nations, the price of software is not a significant issue. However, for underdeveloped nations, the true expenses are frequently excessive. Some academics in developing nations rely on software that has been illegally copied a copy of the software program. There are several benefits to using R, including the possibility of using software packages for free (open source) and the volume and availability of software packages. It is simple to retain and repeat commands on the same data analysis with multiple data frames, facilitating the work of health monitoring officers who frequently analyze data with similar variables but at different times.

An Efficient Big Data Visualization Deep Learning Architecture Model for Path Selection of College Students through Moral Education

Visualization technology can be used to present the analysis results in a more intuitive and easy-to-understand way, which can help educators to better understand the moral education needs of college students, and adjust their teaching strategies accordingly. The combination of big data analysis and visualization technology can also help to improve the efficiency and effectiveness of moral education in colleges and universities. The research on the moral education path selection of college students based on big data visualization has great significance for promoting the development of moral education in colleges and universities, and for cultivating high-quality talent with good moral character. This paper proposed an Optimization model for big data analytics for moral education. The data associated with moral education and information are stored in cloud with the big data. The stored big data visualization process is performed with the optimization model for the feature extraction. The optimization is performed with an integrated Flamingo and weighted black widow Optimization model. The proposed model is stated as the Integrated Flamingo Black Widow (IFBW) model. The performance of the IFBW model is implemented with the deep learning Restricted Boltzmann Machine (RBM) architecture. Simulation analysis stated that IFBW model achieves a higher classification accuracy rate of 99% with a minimal error rate.

Development of Data Labelling and Visualization Technologies for Cultural Heritage Intelligent Curation

This study aims to enable users of digitized cultural heritage artefacts to understand the correlation between artefacts and their context, discover new knowledge, and gain inspiration. We have developed data labelling and visualization technologies specific to cultural heritage so that users, such as curators, cultural heritage experts, and museum visitors, can easily and quickly access the information that they want through the correlation between artefacts. We came up with a web-based labelling tool for cultural heritage data and applied it to data on 45,692 artefacts from the National Museum of Korea. We also invented a web-based visualization tool that converts lists of attribute information in Excel spreadsheets into visualized content and edits the attributes. The meanings of thumbnails of visualized cultural heritage and that of connected lines, a search function, and visualized cultural heritage will allow users to quickly and easily access the cultural heritage information they are looking for.

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology.

Application of data visualization technology in University library service

This paper introduces the role of data visualization in the development of information service in the library, as well as the role in scientific research and teaching. Through the analysis of the research status at home and abroad, it is concluded that the university library can establish a perfect service system in terms of data visualization service from the aspects of providing basic services of data literacy, enriching data information resources, training data librarians and service teams, participating in the whole process of data service, opening up the physical space of data service and establishing the platform of data visualization knowledge exchange. At last, it gives the strategy of developing the data visualization service in the library, including training the data librarian, reconstructing the information service system, realizing the whole-process service method, establishing the multi-participation service platform and carrying out the data literacy education reform. The aim is to provide a preliminary method for the library to carry out data visualization service and improve the library service efficiency.

Literature Review Visualisasi Data dan Sistem Informasi Geografis

In the past, statisticians, programmers, and data scientists handled data analytics, rarely interacting with companies directly. However, this "experts only" method of data analysis and display has been altered by more user-friendly data visualization, dashboards, and mashup technologies. With improvements that make it possible for people at all levels of an organization to analyze data in meaningful ways, data analytics is being pushed into business. The reporting and visualization tools that statisticians formerly used are now being improved by manufacturers of enterprise-grade analytics. This study describes how dashboards help businesses optimize their operations. And how business dashboards are developed using real-time data and people's innate capacity for visual thought.

Literature Review Visualisasi Data dan Sistem Informasi Geografis

In the past, statisticians, programmers, and data scientists handled data analytics, rarely interacting with companies directly. However, this "experts only" method of data analysis and display has been altered by more user-friendly data visualization, dashboards, and mashup technologies. With improvements that make it possible for people at all levels of an organization to analyze data in meaningful ways, data analytics is being pushed into business. The reporting and visualization tools that statisticians formerly used are now being improved by manufacturers of enterprise-grade analytics. This study describes how dashboards help businesses optimize their operations. And how business dashboards are developed using real-time data and people's innate capacity for visual thought.

Research on the application of big data visualization technology in urban road congestion

ABSTRACT In order to improve the effect of urban road congestion analysis, this paper aims to study the mechanism of traffic congestion diffusion under the condition that users have real-time traffic information, and analyze the urban road congestion situation combined with big data visualization technology. According to the characteristics of traffic flow propagation, this paper conducts multi-granularity abstraction and multi-scale modeling of node-intersection-link-network for the complex and dynamic traffic congestion process, and establishes an improved SIS virus propagation model for traffic congestion propagation. In addition, this paper uses the method of state transition probability to construct an interactive dynamic model of traffic congestion propagation and early warning information propagation in a multi-layer network. The experimental research results show that the big data visualization technology introduced in this paper can play an important role in urban road congestion.

ВІЗУАЛІЗАЦІЯ КОМУНІКАТИВНОГО ПРОЦЕСУ ПІД ЧАС ВИКЛАДАННЯ ІНОЗЕМЦЯМ УКРАЇНСЬКОЇ МОВИ

The article examines the problem of the negative impact on modern young people of a huge amount of information, which many of them are unable to perceive, process and use effectively. It is noted that this problem can be solved by new teaching methods and methods, including the technologies of visual presentation of educational material. The concepts of visibility and visualization were also investigated and it was concluded that these terms denote similar methods of obtaining information based on visual (or visual and auditory) perception of objective reality, but these concepts are not identical. The article presents the possibilities of introducing such popular data visualization technologies as scribing, mental maps, tag clouds and cross-sensing into the process of teaching Ukrainian to foreign students. The rules and algorithms for creating these forms of visual presentation of educational material are analyzed, and their practical application during classroom and out-of-classroom lessons on the Ukrainian language as a foreign language is described. Particular attention is focused on the need to take into account in the process of preparation and practical application of similar forms of visualization not only common features of human thinking, but also cultural and ethnic differences and peculiarities of perception of objects of reality by representatives of different nationalities. A conclusion was made regarding the expediency or impracticality of using scribing, mental maps, tag clouds, cross-senses, as well as interactive timelines and Internet memes as means of communication and presentation of educational information during the teaching of foreign students of the Ukrainian language as such, which take into account psychological and cognitive features of modern youth, allow presenting the material concisely and clearly, and also diversify classes, will contribute to better assimilation and memorization by students of complex grammatical, lexical and linguo-regional studies materials, making the educational process more effective, i.e. meet the requirements of modern education. Key words: visualization, communication, scribing, mental maps, tag clouds, cross-sense.