Database Reference
In-Depth Information
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developers
who design and implement visualization modules, toolkits, sys-
tems, and tools of various sizes and scopes, often adapting and integrating
existing functionality from other visualization toolkits and systems.
In particular, visualization research frequently involves the development of pro-
totypes for evaluating the correctness, flexibility, and performance of new data
processing algorithms and the usability and utility of new interaction techniques.
Facilitating the interdependent needs of novice, savvy, and expert users is
a key part of supporting broader audiences for information visualization. The
number of people who can act as visualization designers or visualization develop-
ers - let alone the core visualization researchers who by necessity often fill these
roles - is rapidly becoming overwhelmed by demand for visual tools brought on
by blossoming public awareness of the power and accessibility of information
visualization techniques. It will become increasingly necessary to provide users
of all skill levels, including novices, with the capability to explore and analyze
data sets of personal and professional interest without direct assistance from
traditional visualization practitioners. However, understanding how to design
accessible yet flexible software artifacts for individual visual exploration and
analysis is only half of this equation. Social organization of visualization roles
through collaboration and other means, as described later in this paper, is the
critical second half.
2.3 Goals
One of the traditional rationales for information visualization is that the human
visual system has high input bandwidth and has evolved as an excellent tool
for spotting patterns and outliers in our surroundings. If we then map large
amounts of data into visual form, we can use these innate human abilities to
explore the data to find patterns that would have been exceedingly dicult to
identify through purely automated techniques. A current prominent example is
bioinformatics research that visually explores gigabytes of gene experiments to
investigate the mechanisms that drive a particular disease. Such “explorative”
use-cases have dominated most of the research in visualization over the past
two decades. Explorative use can either be open-ended, where the user wants
to browse their data without having a predefined question in mind, or analyti-
cally driven, in which the user has a particular question in mind and uses the
visualization to answer it. Often times these two types of exploration will be
intertwined: a user will explore a previously unknown data set without a par-
ticular question in mind, stumble on an interesting data point and then use the
analytic features in the visualization to either answer the question or redirect
their open-ended exploration.
Exploration and Analysis:
Recent visualization environments have begun
to offer users various degrees of interactive control over different parts of the
entire information interface design process, thereby opening up possibilities for
much deeper exploration of data. Such environments allow computer-savvy user-
designers to interactively access data, create, layout, and coordinate views, and
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