Abstract:
We introduce the concept of just-in-time descriptive analytics as a novel
application of computational and statistical techniques performed at
interaction-time to help users easily understand the structure of data as
seen in visualizations. Fundamental to just-in-time descriptive analytics is
(a) identifying visual features, such as clusters, outliers, and trends, user
might observe in visualizations automatically, (b) determining the semantics
of such features by performing statistical analysis as the user is
interacting, and (c) enriching visualizations with annotations that not only
describe semantics of visual features but also facilitate interaction to
support high-level understanding of data. In this paper, we demonstrate
just-in-time descriptive analytics applied to a point-based multi-dimensional
visualization technique to identify and describe clusters, outliers, and
trends. We argue that it provides a novel user experience of computational
techniques working alongside of users allowing them to build faster
qualitative mental models of data by demonstrating its application on a few
use-cases. Techniques used to facilitate just-in-time descriptive analytics
are described in detail along with their run-time performance
characteristics. We believe this is just a starting point and much remains to
be researched, as we discuss open issues and opportunities in improving
accessibility and collaboration.