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comprises 6 dimensions, related to previous work that
investigated the types of questions and tasks
supported by data visualization. This work
contributes to data visualization research in several
ways. First, it explicitly acknowledges annotations as
a first-class citizen in visualization research. It
provides a formal definition of annotations and
introduces an original classification system for
visualization annotations. It then provides a use case
showcasing how this classification can be applied to
qualitatively compare visualizations of the same data.
The resulting classification system is a promising
basis on which the Data Visualization community
might build different long-term realizations, such as
more comprehensive visualization recommender
systems that could propose visualization design
choices based on the types of expected outcomes, or
suggest complementary sets of visual representations
for data based on these outcomes. Future research in
this domain should focus on applying this annotation
classification system to annotations produced on
different datasets represented using various other
visualization idioms, to challenge its completeness
and its generalizability, and possibly further extend it.
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