VisSnippets: A Web-Based System for Impromptu Collaborative Data Exploration on Large Displays

July 26th, 2020

Categories: Applications, Human Factors, Software, User Groups, Tele-Collaboration, Remote Collaboration, Human Computer Interaction (HCI)

An interactive transportation dashboard for SF Muni bus data.
An interactive transportation dashboard for SF Muni bus data.

Authors

Burks, A., Renambot, L., Johnson, A.

About

The VisSnippets system is designed to facilitate effective collaborative data exploration. VisSnippets leverages SAGE2 middleware that enables users to manage the display of digital media content on large displays, thereby providing collaborators with a high-resolution common workspace. Based in JavaScript, VisSnippets provides users with the flexibility to implement and/or select visualization packages and to quickly access data in the cloud. By simplifying the development process, VisSnippets removes the need to scaffold and integrate interactive visualization applications by hand. Users write reusable blocks of code called “snippets” for data retrieval, transformation, and visualization. By composing dataflows from the group’s collective snippet pool, users can quickly execute and explore complementary or contrasting analyses. By giving users the ability to explore alternative scenarios, VisSnippets facilitates parallel work for collaborative data exploration leveraging large-scale displays. We describe the system, its design and implementation, and showcase its flexibility through two example applications.

CCS Concepts: Human-centered computing, Visualization systems and tools; Collaborative and social computing systems and tools.

Keywords: information visualization, visual data science, collaborative visual analytics

Resources

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Citation

Burks, A., Renambot, L., Johnson, A., VisSnippets: A Web-Based System for Impromptu Collaborative Data Exploration on Large Displays, In Practice and Experience in Advanced Research Computing (PEARC ’20), Honorable Mention for Best Student Paper, Portland, OR, ACM, July 26th, 2020. https://doi.org/10.1145/3311790.3396666