  We propose the notion of Attention-Aware Visualizations (AAVs) that track the
user's perception of a visual representation over time and feed this
information back to the visualization. Such context awareness is particularly
useful for ubiquitous and immersive analytics where knowing which embedded
visualizations the user is looking at can be used to make visualizations react
appropriately to the user's attention: for example, by highlighting data the
user has not yet seen. We can separate the approach into three components: (1)
measuring the user's gaze on a visualization and its parts; (2) tracking the
user's attention over time; and (3) reactively modifying the visual
representation based on the current attention metric. In this paper, we present
two separate implementations of AAV: a 2D data-agnostic method for web-based
visualizations that can use an embodied eyetracker to capture the user's gaze,
and a 3D data-aware one that uses the stencil buffer to track the visibility of
each individual mark in a visualization. Both methods provide similar
mechanisms for accumulating attention over time and changing the appearance of
marks in response. We also present results from a qualitative evaluation
studying visual feedback and triggering mechanisms for capturing and
revisualizing attention.
