Database Reference
In-Depth Information
The three sections that follow each take a different approach to suggesting a theory
for Information Visualization. While they were not originally developed with the
above linguistic model in mind, each can be related in some way to this framework.
Natalia Andrienko takes a data-centric view, focusing on the dataset itself, and the
tokens that describe it. She considers how the characteristics of the dataset and the
requirements of the visualization for a task may be matched to determine patterns,
thus predicting the most appropriate visualization tool for the given task. Thus, this
section describes the exploration of the data model so as to identify the best syntax to
use for given tokens (taking into account their referents and the desired semantics).
She highlights the usefulness of systems which can explore the data model, predict
the patterns in datasets, and facilitate the perception of these patterns.
Matthew Ward's starting point is communication theory, and this section is clearly
focused on information content - the meaning of the visualization and maintaining the
flow of information through all stages of the visualization pipeline. He discusses how
we may assess our progress in designing and enhancing visualizations through con-
sidering measurements of information transfer, content or loss, thus providing a useful
theoretical means for validating visualizations. In this case, there is no internal explo-
ration of the data, but it is the validity of the data after transfer from internal model to
external representation that is considered important.
T.J. Jankun-Kelly introduces two useful models for a scientific approach to visu-
alization, both of which are in their infancy. The visual exploration model describes
and captures the dynamic process of user exploration and manipulation of visualiza-
tion in order to affect its redesign, thus using the pragmatic response of the user to
determine a new syntactical arrangement. The second model, visual transformation
design, uses transformation functions applied to the data model to provide design
guidance based on visualization parameters, thus performing an initial exploration of
the data model to suggest syntax to enhance the pragmatic response of the user.
The paper concludes with a summary, and suggestions for future research.
2
Predictive Data-Centered Theory
Among other theories, Information Visualization requires a theory that could serve as
a basis for instructing Information Visualization users how to select the right tools for
their data and do data exploration and analysis with the use of these tools. The same
theory could also help tool designers in finding right solutions. The following argu-
mentation is meant to clarify what kind of theory this could be.
Most Information Visualization researchers agree that the primary purpose for us-
ing Information Visualization tools is to explore data in order to gain understanding of
the data and the phenomena behind. Gaining understanding may be thought of as
constructing a concept, or mental model, of the data or phenomenon. A model, in turn,
can be considered as a parsimonious representation capturing essential features of the
data rather than listing all individual data items; this means that a model necessarily
involves abstraction. For example, from observing morning temperatures over several
days, a person may build a concept of the increase or decrease of the temperature.
Such an abstraction is based on a holistic grasp of characteristic features embrac-
ing multiple data items. We shall use the term “pattern” to refer to such features. In-
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