Towards Autocomplete Strategies for Visualization Construction

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Wei Wei, Samuel Huron, Yvonne Jansen

 Room: 104

2023-10-25T23:54:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T23:54:00Z

Exemplar figure, described by caption below — The three visualization autocomplete strategies identified in our study. The dashed boxes in different colors represent autocomplete suggestions. Based on the token(s) that has been placed, NEXT-STEP provides suggestions for a single next visual mapping operation. GHOST provides situated partial or completed visualization recommendations. GALLERY provides a gallery of completed visual mapping options.

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Keywords

Autocomplete, constructive visualization, visualization authoring, physicalization, automation, expressivity, design.

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.