Expression of Interest
is a project of the
Visualization group of the faculty of Mathematics and Computer Science, Technische Universiteit Eindhoven. The project is supported by the VIEW
programme of the Netherlands Organisation for Scientific Research (NWO)
under research grant no.
The complete project proposal can be found elsewhere here
, on this page an overview is given. The
project has started May 2006, and is carried out by two PhD students.
The Visualization group has developed a number of techniques for data
visualization. On our webpages you can find examples
, also, have a look at SequoiaView
 to see how visual realism can help in
understanding data. The aim of the EOI project is to make a next step,
and to develop more general insights and approaches. One small example
where we think to have made such a step, is our work on zooming and
panning [4, 5].
Data visualization concerns the visual presentation of data such that
users can easily obtain insight . A key
ingredient for successful visualization systems is that they show
interesting aspects in
a clear way. This is obvious, but there is little insight
how this can be achieved. The aim of the project is to develop generic
models and guidelines for this, which form a basis for a number of new
methods and techniques.
The first key question is how users can be enabled to specify their
(current) interest in an effective and efficient way. We propose to
attach to each data element a Degree of Interest (DOI), to be specified
by the user and to be used in later stages of the visualization
Many different ways can be used to specify the DOI. A simple one is
binary selection: The user picks or outlines interesting objects, shown
in the visualization. Another approach is to enable the user to specify
predicates on data values, for instance, by selecting a range of values
for a parameter. More complex methods, such as Boolean expressions, can
be used, but in practice these are often too complex. The main challenge
here is to come up with powerful methods for the support of the user in
the exploration of the data, that are easy to understand and to control.
The second key question is how this user-defined interest can be used
such that the visual attention for each element displayed corresponds to
its importance. Graphic representations of data objects have attributes
like color, shape, size, position, orientation, and texture. A simple
approach to let interesting objects stand out is to parametrize a
graphical attribute that is not used for other purposes to the DOI. For
instance, if all objects are black, color interesting objects
; if all lines are thin, make interesting lines
One can also add graphical elements, for instance by encircling
interesting objects, or by assigning the background locally a
different color. For simple cases, when all objects are
homogeneous or when a single, static visualization is designed,
these approaches work well. But for more complex situations
more powerful approaches are needed, which take full
advantage of the capabilities of the human visual system.
In the third place, visualization is an interactive and
explorative process. How can we enable the user to change his
interest continuously, and thereby to navigate through large
data spaces efficiently and effectively?
New methods will be applied and tested in the context of
software visualization. Software visualization is a challenging
field, where large and complex data-sets have to be handled, and
where multiple views and multiple visualization methods have to
be used. During the analysis of software systems, the interest
of the user will change quickly, and also, just a small part of
the data will be of interest. This urges a need for
visualization methods where interest
is given an explicit
and central role.
Support for Analytical Reasoning
focuses on support of the user while performing analytical
reasoning. An important ingredient is to enable users to capture
interesting aspects. This has led to a prototype system called
]. More information can be found
Evaluation and guidelines
Jing Li focuses
on the development of better guidelines for information
visualization, such that it can be predicted how much attention
each items gets. Evaluation and user testing is important in
this respect. As a first study, the effectiveness of
scatterplots and parallel coordinate plots for judging
correlation has been studied .
J.J. van Wijk. Expression of Interest.
Project proposal NWO, 2005.
J.J. van Wijk and W.A.A. Nuy. Smooth and Efficient
Zooming and Panning. In T. Munzner, S. North (eds.),
Proceedings IEEE Symposium on Information
Visualization (InfoVis'2003), IEEE Computer Society
Press, October 2003, p. 15-22. Best paper award.
Wijk, J.J. van, Wim A.A. Nuij. A Model for Smooth
Viewing and Navigation of Large 2D Information
Spaces. IEEE Transactions on Visualization and
Computer Graphics, vol 10 no 4, July-August 2004, p.
J.J. van Wijk. The Value of Visualization.
In C. Silva, E. Gröller, and H. Rushmeier (eds.),
Proceedings IEEE Visualization 2005.
Y.B. Shrinivasan Y.B, and J.J.
van Wijk. Supporting the Analytical Reasoning
Process in Information Visualization, to appear
in ACM Human Factors in Computing Systems (CHI),
J. Li, J.-B. Martens, J.J. van Wijk. Judging
Correlation from Scatterplots and Parallel
Coordinate Plots. Submitted to Information