Title: Foresight: Recommending Visual Insights,
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 system
that helps the user rapidly discover visual insights from large
high-dimensional datasets. Formally, an "insight" is a strong manifestation of
a statistical property of the data, e.g., high correlation between two
attributes, high skewness or concentration about the mean of a single
attribute, a strong clustering of values, and so on. For each insight type,
Foresight initially presents visualizations of the top k instances in the data,
based on an appropriate ranking metric. The user can then look at "nearby"
insights by issuing "insight queries" containing constraints on insight
strengths and data attributes. Thus the user can directly explore the space of
insights, rather than the space of data dimensions and visual encodings as in
other visual recommender systems. Foresight also provides "global" views of
insight space to help orient the user and ensure a thorough exploration
process. Furthermore, Foresight facilitates interactive exploration of large
datasets through fast, approximate sketching.