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| 1 | ECharts: A declarative framework for rapid construction of web-based visualization显示文摘While there have been a dozen of authoring systems and programming toolkits for visual design and development,users who do not have programming skills,such as data analysts or interface designers,still may feel cumbersome to efficiently implement a web-based visualization.In this paper,we present ECharts,an open-sourced,web-based,cross-platform framework that supports the rapid construction of interactive visualization.The motivation is driven by three goals:easy-touse,rich built-in interactions,and high performance.The kernel of ECharts is a suite of declarative visual design language that customizes built-in chart types.The underlying streaming architecture,together with a high-performance graphics renderer based on HTML5 canvas,enables the high expandability and performance of ECharts.We report the design,implementation,and applications of ECharts with a diverse variety of examples.We compare the utility and performance of ECharts with C3.js,HighCharts,and Chart.js.Results of the experiments demonstrate the efficiency and scalability of our framework.Since the first release in June 2013,ECharts has iterated 63 versions,and attracted over 22,000 star counts and over 1700 related projects in the GitHub.ECharts is regarded as a leading visualization development tool in the world,and ranks the third in the GitHub visualization tab. | Deqing Li Honghui Mei Yi Shen Shuang Su Wenli Zhang Junting Wang Ming Zu Wei Chen | 2018 | Visual Informatics2018，2，2： | 69 |
| 2 | Metaverse:Perspectives from graphics,interactions and visualization显示文摘The metaverse is a visual world that blends the physical world and digital world.At present,the development of the metaverse is still in the early stage,and there lacks a framework for the visual construction and exploration of the metaverse.In this paper,we propose a framework that summarizes how graphics,interaction,and visualization techniques support the visual construction of the metaverse and user-centric exploration.We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline.We propose a taxonomy of interaction technologies based on interaction tasks,user actions,feedback and various sensory channels,and a taxonomy of visualization techniques that assist user awareness.Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse.We hope this paper can provide a stepping stone for further research in the area of graphics,interaction and visualization in the metaverse. | Yuheng Zhao Jinjing Jiang Yi Chen Richen Liu Yalong Yang Xiangyang Xue Siming Chen | 2022 | Visual Informatics2022，6，1： | 10 |
| 3 | Towards better analysis of machine learning models:A visual analytics perspective显示文摘Interactive model analysis,the process of understanding,diagnosing,and refining a machine learning model with the help of interactive visualization,is very important for users to efficiently solve real-world artificial intelligence and data mining problems.Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks.In this paper,we present a comprehensive analysis and interpretation of this rapidly developing area.Specifically,we classify the relevant work into three categories:understanding,diagnosis,and refinement.Each category is exemplified by recent influential work.Possible future research opportunities are also explored and discussed. | Shixia Liu Xiting Wang Mengchen Liu Jun Zhu | 2017 | Visual Informatics2017，1，1： | 9 |
| 4 | PerformanceVis:Visual analytics of student performance data from an introductory chemistry course显示文摘We present PerformanceVis,a visual analytics tool for analyzing student admission and course performance data and investigating homework and exam question design.Targeting a university-wide introductory chemistry course with nearly 1000 student enrollment,we consider the requirements and needs of students,instructors,and administrators in the design of PerformanceVis.We study the correlation between question items from assignments and exams,employ machine learning techniques for student grade prediction,and develop an interface for interactive exploration of student course performance data.PerformanceVis includes four main views(overall exam grade pathway,detailed exam grade pathway,detailed exam item analysis,and overall exam&homework analysis)which are dynamically linked together for user interaction and exploration.We demonstrate the effectiveness of PerformanceVis through case studies along with an ad-hoc expert evaluation.Finally,we conclude this work by pointing out future work in this direction of learning analytics research. | Haozhang Deng Xuemeng Wang Zhiyi Guo Ashley Decker Xiaojing Duan Chaoli Wang G.Alex Ambrose Kevin Abbott | 2019 | Visual Informatics2019，3，4： | 5 |
| 5 | NetV.js:A web-based library for high-efficiency visualization of large-scale graphs and networks显示文摘Graph visualization plays an important role in several fields,such as social media networks,protein-protein interaction networks,and traffic networks.A number of visualization design tools and programming toolkits have been widely used in graph-related applications.However,a key challenge remains in the high-efficiency visualization of large-scale graph data.In this study,we present NetV.js,an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data(up to 50 thousand nodes and 1 million edges)at an interactive frame rate with a commodity computer.Experimental results demonstrate that our library outperforms existing toolkits(Sigma.js,D3.js,Cytoscape.js,and Stardust.js)in terms of performance. | Dongming Han Jiacheng Pan Xiaodong Zhao Wei Chen | 2021 | Visual Informatics2021，5，1： | 5 |
| 6 | Steering data quality with visual analytics:The complexity challenge显示文摘Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans. | Shixia Liu Gennady Andrienko Yingcai Wu Nan Cao Liu Jiang Conglei Shi Yu-Shuen Wang Seokhee Hong | 2018 | Visual Informatics2018，2，4： | 5 |
| 7 | VisComposer: A Visual Programmable Composition Environment for Information Visualization显示文摘As the amount of data being collected has increased,the need for tools that can enable the visual exploration of data has also grown.This has led to the development of a variety of widely used programming frameworks for information visualization.Unfortunately,such frameworks demand comprehensive visualization and coding skills and require users to develop visualization from scratch.An alternative is to create interactive visualization design environments that require lttle to no programming.However,these tools only supports a small portion of visual formns.We present a programmable integrated development environment(IDE),VisComposer,that supports the development of expressive visualization using a drag and-drop visual interface.Vis-Composer exposes the programmability by customizing desired components within a modular-ized visualization composition pipeline,effectively balancing the capability gap between expert coders and visualization artists.The implemented system empowers users to compose compre-bensive visualizations with real-time preview and optimization features,and supports prototyp-ing,sharing and reuse of the effects by means of an intuitive visual composer.Visual program-ming and textual programming integrated in our system allow users to compose more complex visual effects while retaining the simplicity of use.We demonstrate the performance of VisCom-poser with a variety of examples and an informal user evaluation. | Honghui Mei Wei Chen Yuxin Ma Huihua Guan Wanqi Hu | 2018 | Visual Informatics2018，2，1： | 4 |
| 8 | Recent advances in transient imaging:A computer graphics and vision perspective显示文摘Transient imaging has recently made a huge impact in the computer graphics and computer vision fields.By capturing,reconstructing,or simulating light transport at extreme temporal resolutions,researchers have proposed novel techniques to show movies of light in motion,see around corners,detect objects in highly-scattering media,or infer material properties from a distance,to name a few.The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution,infor-mation usually lost during the capture-time temporal integration.This paper presents recent advances in this field of transient imaging from a graphics and vision perspective,including capture techniques,analysis,applications and simulation. | Adrian Jarabo Belen Masia Julio Marco Diego Gutierrez | 2017 | Visual Informatics2017，1，1： | 4 |
| 9 | MessageLens:A Visual Analytics System to Support Multifaceted Exploration of MOOC Forum Discussions显示文摘Massive Open Online Courses(MOOCs)often provide online discussion forum tools to facilitate learner interaction and communication.Having massive forum messages posted by learners everyday,MOOC forums are regarded as an important source for understanding learners activities and opinions.However,the high volume and heterogeneity of MOOC forum contents make it challenging to analyze forum data effectively from different perspectives of discussions and to integrate diverse information into a coherent understanding of issues of concern.In this paper,we report a study on the design of a visual analytics tool to facilitate the multifaceted analysis of online discussion forums.This tool,called MessageLens,aims at helping MOOC instructors to gain a better understanding of forum discussions from three facets:discussion topic,learner attitude,and communication among learners.With various visualization tools,instructors can investigate learner activities from different perspectives.We report a case study with real-world MOOC forum data to present the features of MessageLens and a preliminary evaluation study on the benefits and areas of improvement of the system.Our research suggests an approach to analyzing rich communication contents as well as dynamic social interactions among people. | Jian-Syuan Wong Xiaolong'Luke”Zhang | 2018 | Visual Informatics2018，2，1： | 3 |
| 10 | Support-free interior carving for 3D printing显示文摘Recent interior carving methods for functional design necessitate a cumbersome cut-and-glue process in fabrication.Wepropose a method to generate interior voids which not only satisfy the functional purposes but are also support-free during the 3D printing process.We introduce a support-free unit structure for voxelization and derive the wall thicknesses parametrization for continuous optimization.Wealso design a discrete dithering algorithm to ensure the printability of ghost voxels.The interior voids are iteratively carved by alternating the optimization and dithering.We apply our method to optimize the static and rotational stability,and print various results to evaluate the efficacy. | Yue Xie Xiang Chen | 2017 | Visual Informatics2017，1，1： | 3 |
| 11 | Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations,clustering,and active learning显示文摘Supervised machine learning techniques require labelled multivariate training datasets.Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations.Using appropriate techniques,analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions.While this principle has been implemented either for unsupervised,semi-supervised,or supervised machine learning tasks,the combination of all three methodologies remains challenging.In this paper,a visual analytics approach is presented,combining a variety of machine learning capabilities with four linked visualisation views,all integrated within the mVis(multivariate Visualiser)system.The available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions,from which a classifier can be built.In the workflow,the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning.Once a dataset has been interactively labelled,the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training dataset.Using a novel technique called automatic dimension selection,interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms.A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks,from initial labelling through iterations of data exploration,clustering,classification,and active learning to refine the named partitions,to finally producing a high-quality labelled training dataset suitable for training a classifier.The tool empowers the analyst with interactive visualisations including scatterplots,parallel coordinates,similarity maps for records,and a new similarity map for partitions. | Mohammad Chegini Jürgen Bernard Philip Berger Alexei Sourin Keith Andrews Tobias Schreck | 2019 | Visual Informatics2019，3，1： | 3 |
| 12 | Visual analytics of taxi trajectory data via topical sub-trajectories显示文摘GPS-based taxi trajectories contain valuable knowledge about movement patterns for transportation and urban planning.Topic modeling is an effective tool to extract semantic information from taxi trajectory data.However,previous methods generally ignore trajectory directions that are important in the analysis of movement patterns.In this paper,we employ the bigram topic model rather than traditional topic models to analyze textualized trajectories and consider the direction information of trajectories.We further propose a modified Apriori algorithm to extract topical sub-trajectories and use them to represent each topic.Finally,we design a visual analytics system with several linked views to facilitate users to interactively explore movement patterns from topics and topical sub-trajectories.The case studies with Chengdu taxi trajectory data demonstrate the effectiveness of the proposed system. | Huan Liu Sichen Jin Yuyu Yan Yubo Tao Hai Lin | 2019 | Visual Informatics2019，3，3： | 3 |
| 13 | Exploring the design space of immersive urban analytics显示文摘Recent years have witnessed the rapid development and wide adoption of immersive head-mounted devices,such as HTC VIVE,Oculus Rift,and Microsoft HoloLens.These immersive devices have the potential to significantly extend the methodology of urban visual analytics by providing critical 3D context information and creating a sense of presence.In this paper,we propose a theoretical model to characterize the visualizations in immersive urban analytics.Furthermore,based on our comprehensive and concise model,we contribute a typology of combination methods of 2D and 3D visualizations that distinguishes between linked views,embedded views,and mixed views.We also propose a supporting guideline to assist users in selecting a proper view under certain circumstances by considering visual geometry and spatial distribution of the 2D and 3D visualizations.Finally,based on existing work,possible future research opportunities are explored and discussed. | Zhutian Chen Yifang Wang Tianchen Sun Xiang Gao Wei Chen Zhigeng Pan Huamin Qu Yingcai Wu | 2017 | Visual Informatics2017，1，2： | 3 |
| 14 | DataV:Data Visualization on large high-resolution displays显示文摘In recent years,the technology and applications of visualizations on large high-resolution displays(LHDs)have received widespread attention because of its perceptual benefits and improved productivity.However,existing work on LHD visualization lacks both comprehensive guidance for design requirements and tools developed for its specific usage scenarios.In this paper,we present the scenarios,design,and implementation of DataV,a Software-as-a-Service(SaaS)visual deployment tool that enables rapid construction and cross-platform publishing of interactive visualization on LHDs.Our framework can support rich components for the high-performance rendering of multisource heterogeneous data.DataV provides a full-fledged toolchain to help the user efficiently specify layout and interactions.We present its accessibility and impressive visual effects with examples and comparison with Tableau,Power BI,VisComposer,and iVisDesigner.We also report the performance of using DataV for 3D map rendering by comparing it with deck.gl. | Honghui Mei Huihua Guan Chengye Xin Xiao Wen Wei Chen | 2020 | Visual Informatics2020，4，3： | 3 |
| 15 | Toward automatic comparison of visualization techniques:Application to graph visualization显示文摘Many end-user evaluations of data visualization techniques have been run during the last decades.Their results are cornerstones to build efficient visualization systems.However,designing such an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility.We believe that modern and efficient computer vision techniques,such as deep convolutional neural networks(CNNs),may help visualization researchers to build and/or adjust their evaluation hypothesis.The basis of our idea is to train machine learning models on several visualization techniques to solve a specific task.Our assumption is that it is possible to compare the efficiency of visualization techniques based on the performance of their corresponding model.As current machine learning models are not able to strictly reflect human capabilities,including their imperfections,such results should be interpreted with caution.However,we think that using machine learning-based preevaluation,as a pre-process of standard user evaluations,should help researchers to perform a more exhaustive study of their design space.Thus,it should improve their final user evaluation by providing it better test cases.In this paper,we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be.That study compares two mainstream graph visualization techniques:node-link(NL)and adjacency-matrix(AM)diagrams.Using two well-known deep convolutional neural networks,we partially reproduced user evaluations from Ghoniem et al.and from Okoe et al..These experiments showed that some user evaluation results can be reproduced automatically. | L.Giovannangeli R.Bourqui R.Giot D.Auber | 2020 | Visual Informatics2020，4，2： | 2 |
| 16 | A survey on automatic infographics and visualization recommendations显示文摘Automatic infographics generators employ machine learning algorithms/user-defined rules and visual embellishments into the creation of infographics.It is an emerging topic in the field of information visualization that has requirements in many sectors,such as dashboard design,data analysis,and visualization recommendation.The growing popularity of visual analytics in recent years brings increased attention to automatic infographics.This creates the need for a broad survey that reviews and assesses the significant advances in this field.Automatic tools aim to lower the barrier for visually analyzing data by automatically generating visualizations for analysts to search and make a choice,instead of manually specifying.This survey reviews and classifies automatic tools and papers of visualization recommendations into a set of application categories including networkgraph visualizations,annotation visualizations,and storytelling visualization.More importantly,this report presents several challenges and promising directions for future work in the field of automatic infographics and visualization recommendations. | Sujia Zhu Guodao Sun Qi Jiang Meng Zha Ronghua Liang | 2020 | Visual Informatics2020，4，3： | 2 |
| 17 | 3D shape retrieval based on Laplace operator and joint Bayesian model显示文摘Feature analysis plays a significant role in computer vision and computer graphics.In the task of shape retrieval,shape descriptor is indispensable.In recent years,feature extraction based on deep learning becomes very popular,but the design of geometric shape descriptor is still meaningful due to the contained intrinsic information and interpretability.This paper proposes an effective and robust descriptor of 3D models.The descriptor is constructed based on the probability distribution of the normalized eigenfunctions of the Laplace–Beltrami operator on the surface,and a spectrum method for dimensionality reduction.The distance metric of the descriptor space is learned by utilizing the joint Bayesian model,and we introduce a matrix regularization in the training stage to re-estimate the covariance matrix.Finally,we apply the descriptor to 3D shape retrieval on a public benchmark.Experiments show that our method is robust and has good retrieval performance. | Wang Zihao Lin Hongwei | 2020 | Visual Informatics2020，4，3： | 2 |
| 18 | Visual simulation of clouds显示文摘Clouds play an important role when synthesizing realistic images of outdoor scenes.The realistic display of clouds is therefore one of the important research topics in computer graphics.In order to display realistic clouds,we need methods for modeling,rendering,and animating clouds realistically.It is also important to control the shapes and appearances of clouds to create certain visual effects.In this paper,we explain our efforts and research results to meet such requirements,together with related researches on the visual simulation of clouds. | Yoshinori Dobashi Kei Iwasaki Yonghao Yue Tomoyuki Nishita | 2017 | Visual Informatics2017，1，1： | 2 |
| 19 | Enhancing the functionality of augmented reality using deep learning,semantic web and knowledge graphs:A review显示文摘The growth rates of today’s societies and the rapid advances in technology have led to the need for access to dynamic,adaptive and personalized information in real time.Augmented reality provides prompt access to rapidly flowing information which becomes meaningful and‘‘alive’’as it is embedded in the appropriate spatial and time framework.Augmented reality provides new ways for users to interact with both the physical and digital world in real time.Furthermore,the digitization of everyday life has led to an exponential increase of data volume and consequently,not only have new requirements and challenges been created but also new opportunities and potentials have arisen.Knowledge graphs and semantic web technologies exploit the data increase and web content representation to provide semantically interconnected and interrelated information,while deep learning technology offers novel solutions and applications in various domains.The aim of this study is to present how augmented reality functions and services can be enhanced when integrating deep learning,semantic web and knowledge graphs and to showcase the potentials their combination can provide in developing contemporary,user-friendly and user-centered intelligent applications.Particularly,we briefly describe the concept of augmented reality and mixed reality and present deep learning,semantic web and knowledge graphs technologies.Moreover,based on our literature review,we present and analyze related studies regarding the development of augmented reality applications and systems that utilize these technologies.Finally,after discussing how the integration of deep learning,semantic web and knowledge graphs into augmented reality enhances the quality of experience and quality of service of augmented reality applications to facilitate and improve users’everyday life,conclusions and suggestions for future research and studies are given. | Georgios Lampropoulos Euclid Keramopoulos Konstantinos Diamantaras | 2020 | Visual Informatics2020，4，1： | 2 |
| 20 | Interactive map reports summarizing bivariate geographic data显示文摘Bivariate map visualizations use different encodings to visualize two variables but comparison across multiple encodings is challenging.Compared to a univariate visualization,it is significantly harder to read regional differences and spot geographical outliers.Especially targeting inexperienced users of visualizations,we advocate the use of natural language text for augmenting map visualizations and understanding the relationship between two geo-statistical variables.We propose an approach that selects interesting findings from data analysis,generates a respective text and visualization,and integrates both into a single document.The generated reports interactively link the visualization with the textual narrative.Users can get additional explanations and have the ability to compare different regions.The text generation process is flexible and adapts to various geographical and contextual settings based on small sets of parameters.We showcase this flexibility through a number of application examples. | Shahid Latif Fabian Beck | 2019 | Visual Informatics2019，3，1： | 2 |
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