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Examining data visualization pitfalls in scientific publications

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 268-282页

作者： Vinh T Nguyen Kwanghee Jung Vibhuti Gupta Department of Information Technology TNU–University of Information and Communication TechnologyThai NguyenVietnam Department of Educational Psychology Leadershipand CounselingTexas Tech UniversityLubbockTX 79409United States Department of Computer Science and data Science Meharry Medical CollegeNashvilleTN 37208USA

data visualization blends art and science to convey stories from data via graphical *** different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full *** the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input *** a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and *** might be even worse if incorrect visual representations were intentionally produced to deceive the *** address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical *** reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial ***,a text mining technique was applied to extract practical insights from common visualization ***’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial *** findings showed that the pie chart is the most misused graphical representation,and size is the most critical *** was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.

关键词： data visualization Graphical representations Misinformation Visual encodings Association rule mining Word cloud Cochran’s Q test McNemar’s test

Improving symbolic data visualization for pattern recognition and knowledge discovery

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Visual Informatics 2020年第1期4卷 23-31页

作者： Kadri Umbleja Manabu Ichino Hiroyuki Yaguchi School of Science and Engineering Tokyo Denki UniversityHatoyamaSaitama 350-0394Japan

This paper examines the visualization of symbolic data and considers the challenges rising from its complex *** data is usually aggregated from large data sets and used to hide entry specific details and to transform huge amounts of data(like big data)into analyzable *** is also used to offer an overview in places where general trends are more important than individual *** data comes in many forms like intervals,histograms,categories and modal multi-valued *** data can also be considered as a ***,the de facto visualization approach for symbolic data is zoomstars which has many *** biggest limitation is that the default distributions(histograms)are not supported in 2D as additional dimension is *** paper proposes several new improvements for zoomstars which would enable it to visualize histograms in 2D by using a quantile or an equivalent interval *** addition,several improvements for categorical and modal variables are proposed for a clearer indication of presented *** for different approaches to zoomstars are offered depending on the data type and the desired ***,an alternative approach that allows visualizing the whole data set in comprehensive table-like graph,called shape encoding,is *** visualizations and their usefulness are verified with three symbolic data sets in exploratory data mining phase to identify trends,similar objects and important features,detecting outliers and discrepancies in the data.

关键词： data visualization Symbolic data Zoomstar Shape encoding Exploratory data analysis

Current state of the art and future directions: Augmented reality data visualization to support decision-making

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Visual Informatics 2024年第2期8卷 80-105页

作者： Mengya Zheng David Lillis Abraham G.Campbell School of Computer Science University College DublinDublinIreland

Augmented Reality(AR),as a novel data visualization tool,is advantageous in revealing spatial data patterns and data-context ***,recent research has identified AR data visualization as a promising approach to increasing decision-making efficiency and *** a result,AR has been applied in various decision support systems to enhance knowledge conveying and comprehension,in which the different data-reality associations have been constructed to aid ***,how these AR visualization strategies can enhance different decision support datasets has not been reviewed *** given the rise of big data in the modern world,this support is critical to decision-making in the coming *** AR to embed the decision support data and explanation data into the end user’s physical surroundings and focal contexts avoids isolating the human decision-maker from the relevant *** the decision-maker’s contexts and the DSS support in AR is a difficult *** paper outlines the current state of the art through a literature review in allowing AR data visualization to support *** facilitate the publication classification and analysis,the paper proposes one taxonomy to classify different AR data visualization based on the semantic associations between the AR data and physical *** on this taxonomy and a decision support system taxonomy,37 publications have been classified and analyzed from multiple *** of the contributions of this literature review is a resulting AR visualization taxonomy that can be applied to decision support *** with this novel tool,the paper discusses the current state of the art in this field and indicates possible future challenges and directions that AR data visualization will bring to support decision-making.

关键词： Augmented reality Decision support data visualization Taxonomy

Exploring the growth value equity valuation model with data visualization

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Financial Innovation 2023年第1期9卷 19-55页

作者： I‑Cheng Yeh Yi‑Cheng Liu Department of Civil Engineering Tamkang University151 Yingzhuan Rd.Tamsui Dist.New Taipei City 25137Taiwan Department of International Business Tamkang University151 Yingzhuan Rd.Tamsui Dist.New Taipei City 25137Taiwan

The Growth Value Model(GVM)proposed theoretical closed form formulas consist-ing of Return on Equity(ROE)and the Price-to-Book value ratio(P/B)for fair stock prices and expected rates of *** regression analysis can be employed to verify these theoretical closed form formulas,they cannot be explored by classical quintile or decile sorting approaches with intuition due to the essence of multi-factors and dynamical *** article uses visualization techniques to help intuitively explore *** discerning findings and contributions of this paper is that we put forward the concept of the smart frontier,which can be regarded as the reasonable lower limit of P/B at a specific ROE by exploring fair P/B with ROE-P/B 2D dynamical process *** coefficients in the formula can be determined by the quantile regression analysis with market *** moving paths of the ROE and P/B in the cur-rent quarter and the subsequent quarters show that the portfolios at the lower right of the curve approaches this curve and stagnates here after the portfolios are ***,exploring expected rates of return with ROE-P/B-Return 3D dynamical process visualization,the results show that the data outside of the lower right edge of the“smart frontier”has positive quarterly return rates not only in the t+1 quarter but also in the t+2 *** farther away the data in the t quarter is from the“smart frontier”,the larger the return rates in the t+1 and t+2 quarter.

关键词： data visualization Stock prices Rates of return Return on equity Price-tobook value ratio

data visualization in Big data Analysis: Applications and Future Trends

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Journal of Computer and Communications 2024年第11期12卷 76-85页

作者： Wenyi Ouyang Guangdong Overseas Chinese High School Guangzhou China

The advent of the big data era has made data visualization a crucial tool for enhancing the efficiency and insights of data analysis. This theoretical research delves into the current applications and potential future trends of data visualization in big data analysis. The article first systematically reviews the theoretical foundations and technological evolution of data visualization, and thoroughly analyzes the challenges faced by visualization in the big data environment, such as massive data processing, real-time visualization requirements, and multi-dimensional data display. Through extensive literature research, it explores innovative application cases and theoretical models of data visualization in multiple fields including business intelligence, scientific research, and public decision-making. The study reveals that interactive visualization, real-time visualization, and immersive visualization technologies may become the main directions for future development and analyzes the potential of these technologies in enhancing user experience and data comprehension. The paper also delves into the theoretical potential of artificial intelligence technology in enhancing data visualization capabilities, such as automated chart generation, intelligent recommendation of visualization schemes, and adaptive visualization interfaces. The research also focuses on the role of data visualization in promoting interdisciplinary collaboration and data democratization. Finally, the paper proposes theoretical suggestions for promoting data visualization technology innovation and application popularization, including strengthening visualization literacy education, developing standardized visualization frameworks, and promoting open-source sharing of visualization tools. This study provides a comprehensive theoretical perspective for understanding the importance of data visualization in the big data era and its future development directions.

关键词： data visualization Big data Analysis Artificial Intelligence Interactive visualization data-Driven Decision Making

data visualization with Probabilistic Clustering and Neighbor Embedding

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Data Visualization with Probabilistic Clustering and Neighbo...

Design of a Web Crawler for Water Quality Monitoring data and data visualization

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作者： Xiaohui Liao Jingqi Yan Department of Automation Shanghai Jiao Tong University

In the era of information explosion, processing and analyzing large-scale and high-dimensional data sets has become a big challenge for data mining and machine learning. In order to obtain and intuitively understand the information underlying the big data, an effective visualization technique is on demand. Many successful visualization techniques project high-dimensional data sets into low-dimensional spaces so that we can present data points in scatter plots, histograms or parallel coordinate *** this paper, we propose a new algorithm called PCNE, the algorithm first performs a probabilistic clustering algorithm for coarse classification on the data sets, and then reconstruct the joint probability with the heuristic information of classification results and neighborhood relationship. Our experimental results on the public data sets demonstrate that the PCNE algorithm outperforms the classical embedding algorithms in revealing both local and global structures of data.

关键词： data visualization Probabilistic clustering Dimensionality reduction

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Journal on Big data 2022年第2期4卷 135-143页

作者： Ziwen Yu Jianjun Zhang Wenwu Tan Ziyi Xiong Peilun Li Liangqing Meng Haijun Lin Guang Sun Peng Guo College of Engineering and Design Hunan Normal UniversityChangsha410081China LIHERO Technology(Hunan)Co. Ltd.Changsha410205China Big data Institute Hunan University of Finance and EconomicsChangsha410205China University Malaysia Sabah Sabah88400Malaysia

Many countries are paying more and more attention to the protection of water resources at present,and how to protect water resources has received extensive attention from *** quality monitoring is the key work to water resources *** to efficiently collect and analyze water quality monitoring data is an important aspect of water resources *** this paper,python programming tools and regular expressions were used to design a web crawler for the acquisition of water quality monitoring data from Global Freshwater Quality database(GEMStat)sites,and the multi-thread parallelism was added to improve the efficiency in the process of downloading and *** order to analyze and process the crawled water quality data,Pandas and Pyecharts are used to visualize the water quality data to show the intrinsic correlation and spatiotemporal relationship of the data.

关键词： Water quality monitoring data web crawler data visualization

Application of Bayesian Analysis Based on Neural Network and Deep Learning in data visualization

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Journal of Electronic Research and Application 2024年第4期8卷 88-93页

作者： Jiying Yang Qi Long Xiaoyun Zhu Yuan Yang Yunnan Open University Kunming 650224Yunnan ProvinceChina

This study aims to explore the application of Bayesian analysis based on neural networks and deep learning in data *** research background is that with the increasing amount and complexity of data,traditional data analysis methods have been unable to meet the *** methods include building neural networks and deep learning models,optimizing and improving them through Bayesian analysis,and applying them to the visualization of large-scale data *** results show that the neural network combined with Bayesian analysis and deep learning method can effectively improve the accuracy and efficiency of data visualization,and enhance the intuitiveness and depth of data *** significance of the research is that it provides a new solution for data visualization in the big data environment and helps to further promote the development and application of data science.

关键词： Neural network Deep learning Bayesian analysis data visualization Big data environment

Health care data analysis and visualization using interactive data exploration for sportsperson

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Science China(Information Sciences) 2022年第6期65卷 85-97页

作者： Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTíNEZ Rubén González CRESPO Department of Sport and Exercise Sciences Kunsan National University Department of Naval Architecture and Ocean Engineering Ludong University Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja

Sports have scored significant attention among the public in this multifaceted world. Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques. This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training. The need for innovative data exploration methodologies capable of facing Big data’s influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data. This paper presents an interactive healthcare data exploration and visualization(IHDEV) model to enhance multi-scaling data analysis and visualization in the athletic health vision platform. This paper aims to simplify optimization methods to measure sportsperson muscle tension. This model illustrates a three-layer architecture with a raw data acquisition layer, data analysis layer, and visualization layer. The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud. The data analysis layer adapts artificial intelligence(AI) in data mining. The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism, enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training. This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%, prediction ratio of 96.2%, an efficiency ratio of 96.8%, Pearson correlation coefficient of 98.2%, and reduces the error rate of 18.7% compared to other conventional models.

关键词： artificial intelligence health monitoring data visualization data analysis athletics