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Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration

Filip Dabek, Jesus J Caban

    IEEE Transactions on Visualization and Computer Graphics
    |August 12, 2016
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    This study introduces a grammar-based model to understand user interactions with visualization systems. This approach effectively guides users through complex data exploration, enhancing pattern discovery in big data analytics.

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    Area of Science:

    • Computer Science
    • Human-Computer Interaction

    Background:

    • Visual analytics for big data is popular, but user support during multi-dimensional data exploration is lacking.
    • Existing models struggle to capture user interaction, hindering learning from the analytical process.

    Purpose of the Study:

    • To introduce visualization systems that model user interactions for task guidance.
    • To enhance visual data exploration by understanding user difficulties and common patterns.

    Main Methods:

    • A grammar-based model was developed to learn from user interactions.
    • The K-Reversible algorithm was used to identify common patterns among users.
    • Rules were built and applied to provide suggestions for guiding new users.

    Main Results:

    • A formal evaluation with 300 subjects demonstrated the model's effectiveness.
    • The grammar-based model successfully captured users' interactive processes.
    • The approach shows potential for positive impact on user interaction with visualization systems.

    Conclusions:

    • Modeling user interactions is crucial for effective visual data exploration.
    • The proposed grammar-based model offers a promising approach to guide users and enhance pattern discovery.
    • Further research in this area can significantly improve visual analytic systems.