IEEE VIS 2024 Content: Learnable and Expressive Visualization Authoring Through Blended Interfaces

Honorable Mention

Learnable and Expressive Visualization Authoring Through Blended Interfaces

Sehi L'Yi - Harvard Medical School, Boston, United States

Astrid van den Brandt - Eindhoven University of Technology, Eindhoven, Netherlands

Etowah Adams - Harvard Medical School, Boston, United States

Huyen N. Nguyen - Harvard Medical School, Boston, United States

Nils Gehlenborg - Harvard Medical School, Boston, United States

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Room: Bayshore I

2024-10-16T16:00:00ZGMT-0600Change your timezone on the schedule page
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The trade-off between learnability and expressivity has been discussed as an important design consideration for visualization authoring systems. We present Blended Interfaces, a framework for combining multiple authoring interfaces in a complementary way to balance learnability and expressivity.
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Keywords

Visualization authoring, blended interfaces, genomics data visualization

Abstract

A wide range of visualization authoring interfaces enable the creation of highly customized visualizations. However, prioritizing expressiveness often impedes the learnability of the authoring interface. The diversity of users, such as varying computational skills and prior experiences in user interfaces, makes it even more challenging for a single authoring interface to satisfy the needs of a broad audience. In this paper, we introduce a framework to balance learnability and expressivity in a visualization authoring system. Adopting insights from learnability studies, such as multimodal interaction and visualization literacy, we explore the design space of blending multiple visualization authoring interfaces for supporting authoring tasks in a complementary and flexible manner. To evaluate the effectiveness of blending interfaces, we implemented a proof-of-concept system, Blace, that combines four common visualization authoring interfaces—template-based, shelf configuration, natural language, and code editor—that are tightly linked to one another to help users easily relate unfamiliar interfaces to more familiar ones. Using the system, we conducted a user study with 12 domain experts who regularly visualize genomics data as part of their analysis workflow. Participants with varied visualization and programming backgrounds were able to successfully reproduce unfamiliar visualization examples without a guided tutorial in the study. Feedback from a post-study qualitative questionnaire further suggests that blending interfaces enabled participants to learn the system easily and assisted them in confidently editing unfamiliar visualization grammar in the code editor, enabling expressive customization. Reflecting on our study results and the design of our system, we discuss the different interaction patterns that we identified and design implications for blending visualization authoring interfaces.