Leveraging Analysis History for Improved In Situ Visualization Recommendation
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Existing visualization recommendation systems commonly rely on a single snapshot of a dataset to suggest visualizations to users. However, exploratory data analysis involves a series of related interactions with a dataset over time rather than one-off analytical steps. We present Solas, a tool that tracks the history of a user's data analysis, models their interest in each column, and uses this information to provide visualization recommendations, all within the user's native analytical environment. Recommending with analysis history improves visualizations in three primary ways: task-specific visualizations use the provenance of data to provide sensible encodings for common analysis functions, aggregated history is used to rank visualizations by our model of a user's interest in each column, and column data types are inferred based on applied operations. We present a usage scenario and a user evaluation demonstrating how leveraging analysis history improves in situ visualization recommendations on real-world analysis tasks.
Description
CCS Concepts: Human-centered computing --> Visualization; Visualization systems and tools
@article{10.1111:cgf.14529,
journal = {Computer Graphics Forum},
title = {{Leveraging Analysis History for Improved In Situ Visualization Recommendation}},
author = {Epperson, Will and Lee, Doris Jung-Lin and Wang, Leijie and Agarwal, Kunal and Parameswaran, Aditya G. and Moritz, Dominik and Perer, Adam},
year = {2022},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.14529}
}