Towards Autocomplete Strategies for Visualization Construction

TL;DR

This paper explores how autocomplete features can enhance visualization construction by supporting user interaction, proposing three strategies based on a speculative design study to improve efficiency and scalability.

Contribution

It introduces three novel autocomplete strategies for visualization construction and provides design insights from user interaction studies.

Findings

Identified three types of autocomplete strategies.

Gained insights into user preferences for autocomplete features.

Proposed design considerations for future visualization tools.

Abstract

Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality. A free copy of this paper and all supplemental materials are available on our online repository https://osf.io/nu4z3/?view_only=594baee54d114a99ab381886fb32a126