Title: Foresight: Rapid Data Exploration Through Guideposts,
Abstract: Current tools for exploratory data analysis (EDA) require users to manually
select data attributes, statistical computations and visual encodings. This can
be daunting for large-scale, complex data. We introduce Foresight, a
visualization recommender system that helps the user rapidly explore large
high-dimensional datasets through "guideposts." A guidepost is a visualization
corresponding to a pronounced instance of a statistical descriptor of the
underlying data, such as a strong linear correlation between two attributes,
high skewness or concentration about the mean of a single attribute, or a
strong clustering of values. For each descriptor, Foresight initially presents
visualizations of the "strongest" instances, based on an appropriate ranking
metric. Given these initial guideposts, the user can then look at "nearby"
guideposts by issuing "guidepost queries" containing constraints on metric
type, metric strength, data attributes, and data values. Thus, the user can
directly explore the network of guideposts, rather than the overwhelming space
of data attributes and visual encodings. Foresight also provides for each
descriptor a global visualization of ranking-metric values to both help orient
the user and ensure a thorough exploration process. Foresight facilitates
interactive exploration of large datasets using fast, approximate sketching to
compute ranking metrics. We also contribute insights on EDA practices of data
scientists, summarizing results from an interview study we conducted to inform
the design of Foresight.