The Dynamics of Scientific Knowledge - Mapping Scientific Frontiers: The Quest for Knowledge Visualization - page 36

dataset from a variety of perspectives. The mission of information visualization
is well summarized in (Card et al. 1999 ): “ Information visualization is the use of
computer-supported, interactive, visual representations of abstract data to amplify
cognition.”
A common question is the relationship between information visualization and
scientific visualization. A simple answer is that they are unique in terms of their cor-
responding research communities. They do overlap, but largely differ. Here are some
questions that might further clarify the scope of information visualization. First, is
the original data numerical? Graphical depictions of quantitative information are
often seen in the fields of data visualization, statistical graphics, and cartography.
For example, is a plot of daily temperatures of a city for the last 2 years qualified
as information visualization? The answer to this question may depend on another
question: how easy or straightforward is it for someone to produce the plot? As
Michael Friendly and Daniel J. Denis put it, unless you know its history, everything
might seem novel . By the same token, what is complex and novel today may become
trivial in the future. A key point to differentiate information visualization from
data visualization and scientific visualization is down to the presence or absence
of data in quantitative forms and how easy one can transform them to quantitative
forms. This is why researchers emphasize the ability to represent nonvisual data in
information visualization.
Second, if the data is not spatial or quantitative in nature, what does it take to
transform it to something that is spatial and visual? This step involves visual design
and the development of computer algorithms. It is this step that clearly distinguishes
information visualization from its nearest neighbors such as quantitative data
visualization. More formally, this step can be found in an earlier taxonomy of
information visualization, which models the process of information visualization
in terms of data transformation, visualization transformation, and visual map-
ping transformation. Data transformation turns raw data into mathematical forms.
Visualization transformation establishes a visual-spatial model of the data. Visual
mapping transformation determines the appearance of the visual-spatial model to
the user. On the other hand, if the data is quantitative in nature, researchers and
designers are in a better position to capitalize on this valuable given connection.
The connection between scientific and artistic aspects of information visual-
ization is discussed in terms of functional information visualization and aesthetic
information visualization. The primary role of functional information visualization
is to communicate a message to the user, whereas the goal of aesthetic information
visualization is to present a subjective impression of a data set by eliciting a visceral
or emotive response from the user.
The holy grail of information visualization is for users to gain insights .In
general, the notion of insight is broadly defined, including unexpected discoveries, a
deepened understanding, a new way of thinking, eureka-like experiences, and other
intellectual breakthroughs.
In early years of information visualization, it is believed that the ability to view
the entirety of a data set at a glance is important to discover interesting and otherwise
hidden connections and other patterns. More recently, it is realized, with the rise of