Browsing by Author "Perer, Adam"

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    Leveraging Analysis History for Improved In Situ Visualization Recommendation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Epperson, Will; Lee, Doris Jung-Lin; Wang, Leijie; Agarwal, Kunal; Parameswaran, Aditya G.; Moritz, Dominik; Perer, Adam; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
    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.
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    Visualization for Data Scientists: How specific is it?
    (The Eurographics Association, 2020) Santos, Beatriz Sousa; Perer, Adam; Romero, Mario and Sousa Santos, Beatrice
    Data Science has been widely used to support activities in diverse domains as Science, Health, Business, and Sports, to name just a few. Theory and practice have been evolving rapidly, and Data Scientist is currently a position much in demand in the job market. All this creates vast research opportunities, as well as the necessity to better understand how to prepare people as researchers and professionals having the background and skills to keep active in a difficult to anticipate future. While there are courses on Data and Information Visualization described in the literature, as well as recommendations by the SIGGRAPH Education Committee, they do not concern Data Science Programs and thus may not be entirely adequate to this type of Program. Besides the general concepts and methods usually addressed, a Visualization course tailored for this particular audience should probably emphasize specific techniques, tools, and examples of using Visualization in several phases along the Data Science process; moreover, it is reasonable to expect that new approaches, useful in practice, will be proposed by the Visualization research community that should be addressed in such a course. Likewise, the bibliography and teaching methods could probably be adapted. We have analyzed over forty MSc Data Science programs offered in English worldwide, and the Visualization courses most of them include, and we argue that there is a need to adapt existing recommendations and create guidelines for these courses. This panel intends to debate this topic and identify issues that need further reflection.