VAID: Indexing View Designs in Visual Analytics System

TL;DR

This paper introduces VAID, a structured indexing system for visual analytics designs, enabling easier retrieval, understanding, and reuse of complex VA system designs based on user requirements and validation studies.

Contribution

The paper presents VAID, an innovative indexing structure that captures detailed features of VA designs, improving their accessibility and reusability.

Findings

VAID effectively describes complex VA designs with comprehensive labels.

User studies confirm VAID's usefulness for design retrieval and understanding.

VAID enhances the accessibility of professional visualization designs.

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.