29-06-2012, 04:24 PM
Virtual Reality and Abstract Data: Virtualizing Information
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Introduction
The term virtual reality subsumes many different
kinds of activities. Spring(1990) provides a
taxonomy that classifies various efforts along three
dimensions: interaction mode, reality base, and
locus of control. The interaction mode continuum
ranges from symbolic or artificial interactions to
natural interactions. The reality base spans a gamut
from real to constructed realities. The locus of control
dimension goes from total user control to shared
control with any particular user exerting a small
amount of control over the behavior of the system.
Background and Examples of Virtualization
The foundation for virtualization includes work
on scientific visualization, cyberspace design, visual
languages, and hypertext browsers. Scientific
visualization(McCormick,1987; Defanti, Brown, and
McCormick,1989) establishes a baseline for the effort.
While the principles of scientific visualization
are important to virtualization, scientific visualization
has tended to focus on the visualization of data
sets that have some spatial origin; that is, aspects of
the data set are anchored to spatial dimensions.
Wind velocities are plotted on a spatial coordinate
system, the intensity of stellar radiation is plotted on
a stellar map, etc. When the data is inherently spatial,
the problem of depicting data in a virtual space
is in some ways easier -- there are more constraints
and affordances applied to the problem. However,
there is no reason why non spatial data sets can’t be
mapped into a virtual space. The process of mapping
these kinds of data sets is the primary focus of
this article.
The Process of Virtualization
Virtualization is the mapping of an abstract data
set to a virtual space. Mapping abstract data to a
virtual space is a process with which most people
are familiar in a limited context, i.e., many people
have mapped abstract data dimensions to a plane
defined by two orthogonal lines -- a graph, or an XY
plot. However, beyond this mapping of data to XY
spatial coordinates, the process is less familiar
and more difficult. Scientific visualization often incorporates
a third spatial dimension, a fourth time
dimension, and some other visual dimension such as
hue or brightness to represent data. A good example
is weather data on cloud cover.
Developing a metaphor
The development of metaphors owes its heritage
to the development of metaphors for graphical user
interfaces. Whether an underlying metaphor
selected is appropriate is beyond the scope of the
current work. There are general suggestions for the
metaphor--namely that the data space is mapped to
some metaphorical physical space. While we avoid
the suggestion of particular metaphors, we do commend
Kay’s concern with metaphors: