research-article

VAID: Indexing View Designs in Visual Analytics System

Authors:

Yingcai WuAuthors Info & Claims

CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

Article No.: 198, Pages 1 - 15

https://doi.org/10.1145/3613904.3642237

Published: 11 May 2024 Publication History

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.

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Cited By

Deng DZhang CZheng HPu YJi SWu Y(2024)AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial FlowIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345615031:1(492-502)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456150

Index Terms

VAID: Indexing View Designs in Visual Analytics System
1. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

May 2024

18961 pages

ISBN:9798400703300

DOI:10.1145/3613904

Editors:
Florian Floyd Mueller
Monash University
,
Penny Kyburz
The Australian National University
,
Julie R. Williamson
University of Glasgow
,
Corina Sas
Lancaster University
,
Max L. Wilson
University of Nottingham
,
Phoebe Toups Dugas
Monash University/New Mexico State University
,
Irina Shklovski
University of Copenhagen

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Deng DZhang CZheng HPu YJi SWu Y(2024)AdversaFlow: Visual Red Teaming for Large Language Models with Multi-Level Adversarial FlowIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345615031:1(492-502)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456150

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