Data-driven scrollytelling has become a prevalent way of visual communication because of its comprehensive delivery of perspectives derived from the data. However, creating an expressive scrollytelling story requires both data and design literacy and is time-consuming. As a result, scrollytelling has been mainly used only by professional journalists to disseminate opinions. In this paper, we present an automatic method to generate expressive scrollytelling visualization, which can present easy-to-understand data facts through a carefully arranged sequence of views. The method first enumerates data facts of a given dataset, and scores and organizes them. The facts are further assembled, sequenced into a story, with reader input taken into consideration. Finally, visual graphs, transitions, and text descriptions are generated to synthesize the scrollytelling visualization. In this way, non-professionals can easily explore and share interesting perspectives from selected data attributes and fact types. We demonstrate the effectiveness and usability of our method through both use cases and an in-lab user study.

Dashboard visualizations are widely used in data-intensive applications such as business intelligence, operation monitoring, and urban planning. However, existing visualization authoring tools are inefficient in the rapid prototyping of dashboards because visualization expertise and user intention need to be integrated. We propose a novel approach to rapid conceptualization that can construct dashboard templates from exemplars to mitigate the burden of designing, implementing, and evaluating dashboard visualizations. The kernel of our approach is a novel deep learning-based model that can identify and locate charts of various categories and extract colors from an input image or sketch. We design and implement a web-based authoring tool for learning, composing, and customizing dashboard visualizations in a cloud computing environment. Examples, user studies, and user feedback from real scenarios in Alibaba Cloud verify the usability and efficiency of the proposed approach.

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.

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 multi-source 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.

The coronavirus disease 2019 (COVID-19) pandemic started in early 2020. At the beginning of February, a public welfare activity in epidemic data visualization, jointly launched by China Computer Federation (CCF) (CCF) CAD & CG Technical Committee, Alibaba Cloud Tianchi (Alibaba Cloud Tianch), JiqiZhixin (JiqiZhixin), Alibaba Cloud DataV (Alibaba Cloud DataV), and DataWhale (DataWhale), was launched with the theme "Fighting the Epidemic with One Mind and Talents like Tianchi." Developers in general are expected to focus on several demand scenarios, such as epidemic situation display, epidemic popular science, trend prediction, material-supply situation, and rework and return situation of employees from all sectors and areas, to discover the relationship between complex heterogeneous multi-source data, develop various upbeat works and present useful information to the public in a coherent manner. The entry works take the form of data visualization and are divided into two categories: popular science publicity and application scenarios. The popular science publicity category includes works for the public, focused on epidemic situation display, epidemic popular science publicity, epidemic prevention and control, and others. The application scenario category consists of the works of frontline officers, which can provide anti-epidemic workers with effective data tools for efficient and intuitive epidemic analysis; offer reliable, understandable, and easily transmitted information for disease prevention; and assist governments, enterprises, and institutions in the fight against COVID-19.

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-to-use, 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.

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 little to no programming. However, these tools only supports a small portion of visual forms. We present a programmable integrated development environment (IDE), VisComposer, that supports the development of expressive visualization using a drag-and-drop visual interface. VisComposer exposes the programmability by customizing desired components within a modularized visualization composition pipeline, effectively balancing the capability gap between expert coders and visualization artists. The implemented system empowers users to compose comprehensive visualizations with real-time preview and optimization features, and supports prototyping, sharing and reuse of the effects by means of an intuitive visual composer. Visual programming 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 VisComposer with a variety of examples and an informal user evaluation.

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories.

This paper presents an efficient visualization and exploration approach for modeling and characterizing the relationships and uncertainties in the context of a multidimensional ensemble dataset. Its core is a novel dissimilarity-preserving projection technique that characterizes not only the relationships among the mean values of the ensemble data objects but also the relationships among the distributions of ensemble members. This uncertainty-aware projection scheme leads to an improved understanding of the intrinsic structure in an ensemble dataset. The analysis of the ensemble dataset is further augmented by a suite of visual encoding and exploration tools. Experimental results on both artificial and real-world datasets demonstrate the effectiveness of our approach.

Illustrating the differences between DTI fiber models is important for the purposes of group comparison, atlas construction, and uncertainty analysis. Standard approaches show difference between DTI models in 3D space, with either voxel-based or fiber based comparison. We proposes a new method that embeds a high-dimensional 3D fiber model as a continuous map in a 2D normalized space, built on fibers from all targeted fiber models. One advantage over previous 2D embedding approaches is that our method represents all fiber models in a unified coordinate system by taking a cluster-and-project routine, ensuring that closely located fibers from different models will be projected to close points on the 2D map. Using this projected 2D map, subtle differences that are difficult to distinguish in 3D space can be clearly displayed and individual fiber model can be effectively summarized. We applied our method to group comparison and analysis of various fiber models.

Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.