  Most visual analytics systems assume that all foraging for data happens
before the analytics process; once analysis begins, the set of data attributes
considered is fixed. Such separation of data construction from analysis
precludes iteration that can enable foraging informed by the needs that arise
in-situ during the analysis. The separation of the foraging loop from the data
analysis tasks can limit the pace and scope of analysis. In this paper, we
present CAVA, a system that integrates data curation and data augmentation with
the traditional data exploration and analysis tasks, enabling information
foraging in-situ during analysis. Identifying attributes to add to the dataset
is difficult because it requires human knowledge to determine which available
attributes will be helpful for the ensuing analytical tasks. CAVA crawls
knowledge graphs to provide users with a a broad set of attributes drawn from
external data to choose from. Users can then specify complex operations on
knowledge graphs to construct additional attributes. CAVA shows how visual
analytics can help users forage for attributes by letting users visually
explore the set of available data, and by serving as an interface for query
construction. It also provides visualizations of the knowledge graph itself to
help users understand complex joins such as multi-hop aggregations. We assess
the ability of our system to enable users to perform complex data combinations
without programming in a user study over two datasets. We then demonstrate the
generalizability of CAVA through two additional usage scenarios. The results of
the evaluation confirm that CAVA is effective in helping the user perform data
foraging that leads to improved analysis outcomes, and offer evidence in
support of integrating data augmentation as a part of the visual analytics
pipeline.
