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Related Experiment Video

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Bum Chul Kwon, Hannah Kim, Emily Wall

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    AxiSketcher allows users to intuitively guide visual analytics by drawing lines on data. This technique translates sketches into nonlinear axes, reflecting complex domain knowledge for better high-dimensional data exploration.

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    Area of Science:

    • Computer Science
    • Information Visualization

    Background:

    • Visual analytics aids high-dimensional data exploration.
    • Users struggle to integrate domain knowledge, especially high-level insights, into data models due to limited attribute knowledge.

    Purpose of the Study:

    • Introduce AxiSketcher, an interactive technique for users to express domain knowledge via drawings.
    • Enable intuitive steering of data models by interacting with data entries, not just attributes.

    Main Methods:

    • Develop AxiSketcher, an interactive, nonlinear axis mapping approach.
    • Users sketch lines over data points to define desired patterns.
    • The system translates sketches into new, nonlinear axes representing complex user intent.

    Main Results:

    • Demonstrate a novel sketching method for eliciting nonlinear domain knowledge.
    • Present an underlying model for translating user input into meaningful data representations.
    • Introduce an interactive visualization for assessing and refining the generated nonlinear axes.

    Conclusions:

    • AxiSketcher overcomes challenges in expressing complex domain knowledge in visual analytics.
    • Enables users to intuitively impose high-level insights onto data exploration through direct sketching.
    • Facilitates more effective exploration of high-dimensional datasets by aligning visualizations with user expertise.