The Haunted Swamps of Heuristics: Uncertainty in Problem Solving - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 55

simulation parameters into account to provide the confidence in the simulation
outcome. In the proposed solution, users can perform parameter studies through
the World Lines interface to process the input uncertainties. In order to transport
steering information to the underlying data-flow, a novel meta-flow (extension to
a standard data-flow network) is used. The meta flow handles components of the
simulation steering.
World lines are an example of how to handle uncertainty and parameter vari-
ations in a computational-steering environment. Now we move further down the
visualization pipeline, take a look at the visualization-mapping stage and discuss
how an integral view of parameter spaces may influence our view of ensembles of
visualization algorithms.
5.5 Parameter Spaces and Visualization Algorithms
A-space [ 2 ] is a space where all visualization algorithms live. In A-space every algo-
rithm with a specific parameter setting is represented by a unique point. Perturbing
the parameters of an algorithm produces a point set (solution cloud) in A-space. The
solution clouds of two quite different algorithms may overlap. This means that a
visualization algorithm 1 with parameter 1 produces the same or very similar results
as algorithm 2 with parameter 2.
The holistic view of visualization algorithms being embedded in a common space
enables interesting investigations and may lead to novel visualization techniques.
Sample questions are: What is the stability of an algorithm in A-space? Are there
global structures in this space? Can there be smooth transitions between rather diverse
algorithms?What would be sparse blendings between various algorithms? MIDA [ 6 ]
is an interesting example where two well-established volume rendering techniques,
i.e., direct volume rendering (DVR) and maximum intensity projection (MIP), are
combined in a fine-grained fashion. A smooth transition between DVR, MIP and
MIDA itself becomes possible and allows exploiting the strengths of DVR and MIP
while avoiding their weaknesses. Another more coarse-grained combination of visu-
alization algorithms would for example be two-level volume rendering [ 10 ].
5.6 Algorithms, Parameters, Heuristics—Quo Vadis?
Algorithms and their parameters are closely intertwined. They together constitute a
path from the problem to the solution by mapping data to images. Even if parame-
ters are 'just auxiliary measures' they definitely need our help. Heuristic parameter
specification is a viable approach as long as some sort of sensitivity analysis is
taken care of. This sensitivity analysis should not be only done in parameter and
algorithm spaces it should also be extended to data and image spaces. Furthermore
the sensitivity analysis should also be applied to interaction space, as we are often
confronted with interactive visualization applications. An example in this respect is