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In-Depth Information
and cells, organisms, and ecosystems of different
types (Wong and Thomas, 2004). Multidimen-
sional statistical and semantic representations of
data are required for national security. Temporal
analytics of visual and interactive data enable us
to compare the new data to huge digital libraries
of information and discover what might have
changed and why.
According to Wolfgang Kienreich (in Bertschi
et al., 2011), visual analytics is the science of ana-
lytical reasoning facilitated by interactive visual
interfaces; it combines automated analysis, visual
representations, and user interaction in a close
loop intended to provide users with new insights
(Thomas & Cook, 2006). “Visual Analytics builds
upon information visualization to facilitate analyti-
cal reasoning by combining automated discovery
and interactive visualization” (Sabol, in Bertschi et
al., 2011). According to Kienreich (2011), visual
analytics emphasizes the use of visual abstractions
to represent aggregated information and facilitate
the formulation and validation of hypothesis by
expert analysts, while knowledge visualization
emphasizes the use of visual metaphors, facilitates
collaborative dissemination and decision-making
by domain experts. Personal digital universes
(photo collection, etc.) require multimedia search
and retrieval techniques. Human interaction with
visualization solutions supports further reasoning
about the data. Personal social network requires
methods of social network analysis. As stated by
Jusufi, Dingjie, and Kerren (2010), networks are
used in modeling relational data, often comprised
of thousands of nodes and edges, with many at-
tributes to visualize. It is hard to avoid clutter
in network elements in social network analysis,
software engineering, or biochemistry if using
traditional node-link diagrams. Jusufi et al. (2010)
apply a tool called Network Lens to visualize such
attributes in the context of the underlying network;
users can interactively build and combine various
lenses by specifying attributes and selecting suit-
able visual representations.
Kienreich proposes that “Visual Analytics
and Knowledge Visualization join forces for
analyzing, evaluating and, ultimately, utilizing
the wealth of knowledge thus created” with
multi-touch surfaces as a driving technological
factor for a closer integration. Visual analytics
could contribute techniques for the automated
analysis of large amounts of information and the
closed loop approach with analysis, visualization,
and interaction, e.g., with user feedback for con-
sumer sentiment and product quality on a personal
level. Knowledge Visualization could contribute
design- and user-specific visual representations,
e.g., with a map, metro, and aquarium metaphor
for users with limited visual literacy (Kienreich,
in Bertschi et al., 2011, pp. 332-333), as well as
a solar system, or flower metaphor (Van Tonder
& Wesson, 2008). Further developments in visual
analytics result in significant part from the needs
for this type of insight in biology, medicine, envi-
ronmental research, and national security.
3.9. Tag Cloud Visualization
Tag cloud visualization applications became
popular in the social and collaborative software
applications. Tag clouds are text-based visual
representations of a set of tags; the tag importance
is usually depicted by font size, highlighted by
a font color, a background color, or tags are ar-
ranged semantically, according to their similarity.
Interactive tag clouds and tag maps often serve for
multidimensional explorations of large datasets,
where tags are used to label digital content, for
example in photographs (Flickr, www.flickr.com),
videoclips (YouTube, www.youtube.com), WWW
bookmarks (www.del.icio.us), and Instagram
(a free photo and video sharing application for
iPhone or Android apps, with web interfaces).
Interactive tag maps, tag cloud techniques, and
the rapid prototyping methods show geographical
space by providing overviews and filtering by text
and geography (Slingsby, Dykes, Wood, & Clarke,
