Leveraging Analysis History for Improved In Situ Visualization Recommendation

نویسندگان

چکیده

Existing visualization recommendation systems commonly rely on a single snapshot of dataset to suggest visualizations users. However, exploratory data analysis involves series related interactions with over time rather than one-off analytical steps. We present Solas, tool that tracks the history user's analysis, models their interest in each column, and uses this information provide recommendations, all within native environment. Recommending improves three primary ways: task-specific use provenance sensible encodings for common functions, aggregated is used rank by our model column types are inferred based applied operations. usage scenario user evaluation demonstrating how leveraging situ recommendations real-world tasks.

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ژورنال

عنوان ژورنال: Computer Graphics Forum

سال: 2022

ISSN: ['1467-8659', '0167-7055']

DOI: https://doi.org/10.1111/cgf.14529