Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Enabling Collaborative Visual Analysis across Heterogeneous Devices

    Thumbnail
    View/Open
    Badam_umd_0117E_20016.pdf (39.30Mb)
    No. of downloads: 75

    Date
    2019
    Author
    Badam, Sriram Karthik
    Advisor
    Elmqvist, Niklas
    DRUM DOI
    https://doi.org/10.13016/jmz9-sl2g
    Metadata
    Show full item record
    Abstract
    We are surrounded by novel device technologies emerging at an unprecedented pace. These devices are heterogeneous in nature: in large and small sizes with many input and sensing mechanisms. When many such devices are used by multiple users with a shared goal, they form a heterogeneous device ecosystem. A device ecosystem has great potential in data science to act as a natural medium for multiple analysts to make sense of data using visualization. It is essential as today's big data problems require more than a single mind or a single machine to solve them. Towards this vision, I introduce the concept of collaborative, cross-device visual analytics (C2-VA) and outline a reference model to develop user interfaces for C2-VA. This dissertation covers interaction models, coordination techniques, and software platforms to enable full stack support for C2-VA. Firstly, we connected devices to form an ecosystem using software primitives introduced in the early frameworks from this dissertation. To work in a device ecosystem, we designed multi-user interaction for visual analysis in front of large displays by finding a balance between proxemics and mid-air gestures. Extending these techniques, we considered the roles of different devices–large and small–to present a conceptual framework for utilizing multiple devices for visual analytics. When applying this framework, findings from a user study showcase flexibility in the analytic workflow and potential for generation of complex insights in device ecosystems. Beyond this, we supported coordination between multiple users in a device ecosystem by depicting the presence, attention, and data coverage of each analyst within a group. Building on these parts of the C2-VA stack, the culmination of this dissertation is a platform called Vistrates. This platform introduces a component model for modular creation of user interfaces that work across multiple devices and users. A component is an analytical primitive–a data processing method, a visualization, or an interaction technique–that is reusable, composable, and extensible. Together, components can support a complex analytical activity. On top of the component model, the support for collaboration and device ecosystems comes for granted in Vistrates. Overall, this enables the exploration of new research ideas within C2-VA.
    URI
    http://hdl.handle.net/1903/24889
    Collections
    • Computer Science Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility