  Visual analytics systems enable highly interactive exploratory data analysis.
Across a range of fields, these technologies have been successfully employed to
help users learn from complex data. However, these same exploratory
visualization techniques make it easy for users to discover spurious findings.
This paper proposes new methods to monitor a user's analytic focus during
visual analysis of structured datasets and use it to surface relevant articles
that contextualize the visualized findings. Motivated by interactive analyses
of electronic health data, this paper introduces a formal model of analytic
focus, a computational approach to dynamically update the focus model at the
time of user interaction, and a prototype application that leverages this model
to surface relevant medical publications to users during visual analysis of a
large corpus of medical records. Evaluation results with 24 users show that the
modeling approach has high levels of accuracy and is able to surface highly
relevant medical abstracts.
