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

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

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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 representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full force.While 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 data.With 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 decision.It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers.To address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical representations.We 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 orientation.Moreover,a text mining technique was applied to extract practical insights from common visualization pitfalls.Cochran’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 orientation.The findings showed that the pie chart is the most misused graphical representation,and size is the most critical issue.It 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

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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 return.Although 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 processes.This article uses visualization techniques to help intuitively explore GVM.The 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 visualization.The coefficients in the formula can be determined by the quantile regression analysis with market data.The 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 formed.Furthermore,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 quarter.The 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

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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 structure.Symbolic 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 quantities.It is also used to offer an overview in places where general trends are more important than individual details.Symbolic data comes in many forms like intervals,histograms,categories and modal multi-valued objects.Symbolic data can also be considered as a distribution.Currently,the de facto visualization approach for symbolic data is zoomstars which has many limitations.The biggest limitation is that the default distributions(histograms)are not supported in 2D as additional dimension is required.This paper proposes several new improvements for zoomstars which would enable it to visualize histograms in 2D by using a quantile or an equivalent interval approach.In addition,several improvements for categorical and modal variables are proposed for a clearer indication of presented categories.Recommendations for different approaches to zoomstars are offered depending on the data type and the desired goal.Furthermore,an alternative approach that allows visualizing the whole data set in comprehensive table-like graph,called shape encoding,is proposed.These 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

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i2dash:Creation of Flexible,Interactive,and Web-based Dashboards for visualization of Omics data

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 568-577页

作者： Arsenij Ustjanzew Jens Preussner Mette Bentsen Carsten Kuenne Mario Looso Max Planck Institute for Heart and Lung Research Bioinformatics UnitD-61231 Bad NauheimGermany

Data visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data.A suitable visualization has to be intuitive and accessible.Web-based dashboards have become popular tools for the arrangement,consolidation,and display of such visualizations.However,the combination of automated data processing pipelines handling omics data and dynamically generated,interactive dashboards is poorly solved.Here,we present i2dash,an R package intended to encapsulate functionality for the programmatic creation of customized dashboards.It supports interactive and responsive(linked)visualizations across a set of predefined graphical layouts.i2dash addresses the needs of data analysts/software developers for a tool that is compatible and attachable to any R-based analysis pipeline,thereby fostering the separation of Data visualization on one hand and data analysis tasks on the other hand.In addition,the generic design of i2dash enables the development of modular extensions for specific needs.As a proof of principle,we provide an extension of i2dash optimized for single-cell RNA sequencing analysis,supporting the creation of dashboards for the visualization needs of such experiments.Equipped with these features,i2dash is suitable for extensive use in large-scale sequencing/bioinformatics facilities.Along this line,we provide i2dash as a containerized solution,enabling a straightforward large-scale deployment and sharing of dashboards using cloud services.i2dash is freely available via the R package archive CRAN(https://***/package=i2dash).

关键词： Data visualization Dashboard Cloud data analysis Automation

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Filter and Embedded Feature Selection Methods to Meet Big Data visualization Challenges

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Computers, Materials & Continua 2023年第1期74卷 817-839页

作者： Kamal A.ElDahshan AbdAllah A.AlHabshy Luay Thamer Mohammed Mathematics Department Faculty of ScienceAl-Azhar UniversityCairo11884Egypt

This study focuses on meeting the challenges of big Data visualization by using of data reduction methods based the feature selection methods.To reduce the volume of big data and minimize model training time(Tt)while maintaining data quality.We contributed to meeting the challenges of big Data visualization using the embedded method based“Select from model(SFM)”method by using“Random forest Importance algorithm(RFI)”and comparing it with the filter method by using“Select percentile(SP)”method based chi square“Chi2”tool for selecting the most important features,which are then fed into a classification process using the logistic regression(LR)algorithm and the k-nearest neighbor(KNN)algorithm.Thus,the classification accuracy(AC)performance of LRis also compared to theKNN approach in python on eight data sets to see which method produces the best rating when feature selection methods are applied.Consequently,the study concluded that the feature selection methods have a significant impact on the analysis and visualization of the data after removing the repetitive data and the data that do not affect the goal.After making several comparisons,the study suggests(SFMLR)using SFM based on RFI algorithm for feature selection,with LR algorithm for data classify.The proposal proved its efficacy by comparing its results with recent literature.

关键词： data Redaction features selection Select from model Select percentile big Data visualization Data visualization

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WebScope: A New Tool for Fusion data Analysis and visualization

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Plasma Science and Technology 2010年第2期12卷 253-256页

作者：杨飞党宁宁肖炳甲 Institute of Plasma Physics Chinese Academy of Sciences Computer center of Anhui Medical University

A visualization tool was developed through a web browser based on Java applets embedded into HTML pages, in order to provide a world access to the EAST experimental data. It can display data from various trees in different servers in a single panel. With WebScope, it is easier to make a comparison between different data sources and perform a simple calculation over different data sources.

关键词： WebScope EAST MDSplus Data visualization Java applet

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Designing,Analysis and Implementation of Open-source visualization System for High-Definition Remote Sensing data Processing

Designing,Analysis and Implementation of Open-source Visuali...

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第32届中国控制与决策会议

作者： HAN Peng LI Yan WANG Ying LI Linqi MA Haotian WANG Jinkuan School of Computer and Communication Engineering Northeastern University at Qinhuangdao School of Control Engineering Northeastern University at Qinhuangdao

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

The big data characteristics of Marine High-Definition Remote Sensing（MHDRS） has result in the obstacle of the real-time processing,effective decision-making and low efficiency information collection.Aiming at this problem,this paper analyzes the visualization and control integrate system,and establishes a visually analytic system to process the different source of Remote Sensing（RS） data as well as its storage based on the MHDRS data.The result shows that the new method enhances the analysis and sharing with high efficiency.

关键词： RS Images Geographic Information System Data visualization Open Source System B/S structure

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Data visualization with Probabilistic Clustering and Neighbor Embedding

Data Visualization with Probabilistic Clustering and Neighbo...

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第37届中国控制会议

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 plots.In 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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Design of a Web Crawler for Water Quality Monitoring data and Data visualization

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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 society.Water quality monitoring is the key work to water resources protection.How to efficiently collect and analyze water quality monitoring data is an important aspect of water resources protection.In 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 parsing.In 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

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