Honorable Mention Award

We present an authoring tool, called CAST+ (Canis Studio Plus), that enables the interactive creation of chart animations through the direct manipulation of keyframes. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis (Ge et al. 2020), a declarative chart animation grammar that leverages data-enriched SVG charts, CAST+ supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation. We report a user study conducted to assess the visual specification and system usability with its initial version. We enhanced the system's expressiveness and usability: CAST+ now supports the animation of multiple types of visual marks in the same keyframe group with new auto-completion algorithms based on generalized selection. This enables the creation of more expressive animations, while reducing the number of interactions needed to create comparable animations. We present a gallery of examples and four usage scenarios to demonstrate the expressiveness of CAST+. Finally, we discuss the limitations, comparison, and potentials of CAST+ as well as directions for future research.

We present a novel multi-level representation of time series called OM3 that facilitates efficient interactive progressive visualization of large data stored in a database and supports various interactions such as resizing, panning, zooming, and visual query. Based on our proposed line-segment aggregation, this representation can produce error-free line visualizations that preserve the shape of a time series in windows of arbitrary sizes. To reduce the interaction latency, we develop an incremental tree-based query strategy to support progressive visualizations, allowing a finer control on the accuracy-time tradeoff. We quantitatively compare OM3 with state-of-the-art methods, including a method implemented on a leading time-series database InfluxDB, in two settings with databases residing either in the local area network or on the cloud. Results show that OM^3 maintains a low latency within 300~ms on the web browser and a high data reduction ratio regardless of the data size (ranging from millions to billions of records), achieving around 1,000 times faster than the state-of-the-art methods on the largest dataset experimented with.

Time-series data-usually presented in the form of lines-plays an important role in many domains such as finance, meteorology, health, and urban informatics. Yet, little has been done to support interactive exploration of large-scale time-series data, which requires a clutter-free visual representation with low-latency interactions. In this paper, we contribute a novel line-segment-based KD-tree method to enable interactive analysis of many time series. Our method enables not only fast queries over time series in selected regions of interest but also a line splatting method for efficient computation of the density field and selection of representative lines. Further, we develop KD-Box, an interactive system that provides rich interactions, e.g., timebox, attribute filtering, and coordinated multiple views. We demonstrate the effectiveness of KD-Box in supporting efficient line query and density field computation through a quantitative comparison and show its usefulness for interactive visual analysis on several real-world datasets.

Abstract In this paper, we introduce Canis, a high‐level domain‐specific language that enables declarative specifications of data‐driven chart animations. By leveraging data‐enriched SVG charts, its grammar of animations can be applied to the charts created by existing chart construction tools. With Canis, designers can select marks from the charts, partition the selected marks into mark units based on data attributes, and apply animation effects to the mark units, with the control of when the effects start. The Canis compiler automatically synthesizes the Lottie animation JSON files [Aira], which can be rendered natively across multiple platforms. To demonstrate Canis’ expressiveness, we present a wide range of chart animations. We also evaluate its scalability by showing the effectiveness of our compiler in reducing the output specification size and comparing its performance on different platforms against D3.