VAID: Indexing View Designs in Visual Analytics System

TL;DR

VAID addresses the challenge of querying and reusing complex visual analytics designs by introducing a structured, expressive index for VA designs. The approach combines a Task+Design index with a Vega-Lite-inspired JSON representation to describe analytical tasks and visual encodings, extended to handle composite visuals and graph-related marks. The authors validate VAID through a workshop with 12 VA designers, annotation of 442 view designs from 124 VA systems, and a question-based user study using a VAID Explorer prototype, showing usefulness and insights into VA design patterns. They discuss opportunities, limitations, and future directions, highlighting VAID as a foundation for more accessible, reusable VA designs and potential automation with AI and open-source data.

Abstract

Visual analytics (VA) systems have been widely used in various application domains. However, VA systems are complex in design, which imposes a serious problem: although the academic community constantly designs and implements new designs, the designs are difficult to query, understand, and refer to by subsequent designers. To mark a major step forward in tackling this problem, we index VA designs in an expressive and accessible way, transforming the designs into a structured format. We first conducted a workshop study with VA designers to learn user requirements for understanding and retrieving professional designs in VA systems. Thereafter, we came up with an index structure VAID to describe advanced and composited visualization designs with comprehensive labels about their analytical tasks and visual designs. The usefulness of VAID was validated through user studies. Our work opens new perspectives for enhancing the accessibility and reusability of professional visualization designs.

Figures (9)

• Figure 1: The frequency of users' preference rankings for data, tasks, and visualization. For instance, 6 participants ranked "Data" as their top choice.
• Figure 2: A VA task consists of a dual-key index. The action's value is selected from four classes, with each class having a single subclass chosen, so as the target's value. The task "enjoy + values" is exemplified in red strokes.
• Figure 3: We follow a Vega-Lite style to describe complex view designs. (A) Our formal index structure for visual designs and (B) an example with the index. The structure for representing each chart, including marks and encodings, is simplified and indicated using "[] part" with black text. To illustrate, we provide an example of the bar part in the upper left corner.
• Figure 4: Example indexes of (A) graph-related visualizations and (B) visualizations with different compositions.
• Figure 5: The VAID Explorer contains a filtering view (A), an indexing view (B), a gallery view (C), and a detail view (D).