# VAID: Indexing View Designs in Visual Analytics System

arXiv: https://arxiv.org/abs/2211.02567 · HTML: https://mlsanity.com/paper/2211.02567/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2022.

## Built on by (most cited first, 5 of 5)

- [Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM](https://mlsanity.com/paper/2410.14879/) (2024)
- [An Image-based Typology for Visualization](https://mlsanity.com/paper/2403.05594/) (2024)
- [Safire: Similarity Framework for Visualization Retrieval](https://mlsanity.com/paper/2510.16662/) (2025)
- [VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design](https://mlsanity.com/paper/2508.07497/) (2025)
- [Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation](https://mlsanity.com/paper/2607.24766/) (2026)

## Abstract (arXiv)

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.