The Haunted Swamps of Heuristics: Uncertainty in Problem Solving - Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization - page 52

Fig. 5.4 Algorithm and its
parameters. a Algorithm
without parameters. b
Parameters on top of the
algorithm. c Yin and Yang
union of algorithm and its
parameters

(a)

algorithm

data image

(b)

parameters

data image

algorithm

(c)

data image

undue burden on the user and impacts the usability and applicability of an algorithm.
Finding a path, i.e., solving a problem, requires an algorithm and parameters. Visual-
ization research is often concerned with taking data and producing an image as visual
result. This mapping is realized through an algorithm. But as said above an algorithm
alone (like in Fig. 5.4 a) is typically not sufficient by itself. Also an algorithm and its
parameters do not live side-by-side or one is on top of the other (like in Fig. 5.4 b).
An algorithm and its parameters are closely intertwined in a yin yang union (like in
Fig. 5.4 c). Changing an algorithm has an immediate impact on the pertaining para-
meters, some of them may even vanish or new ones might come into existence. On
the other hand, changing parameters may heavily impact the functioning of an algo-
rithm. The results may even be similar to the results from a quite different algorithm
(with other parameters of its own). In various disciplines parameter-space analyses
are already well established to determine the robustness and stability of processes or
procedures. In the area of visualization the investigation of parameters and the spaces
they live in has gained increased interest only in recent years. Knowledge-assisted
visualization or the visualization of variations and ensembles go into this direction.
In the next section we will discuss parameter-space analysis in more detail.

5.4 Parameter-Space Analysis

Newand improved imagingmodalities, like dual energy computed tomography, allow
measuring the same specimen with varying parameters. Increased computing perfor-
mance (multi-core CPUs, GPUs) allows not only calculating one simulation run but
hundreds or even thousands of runs with changing parameter settings. This necessi-
tates investigating and visualizing large sets of simulations or data ensembles at the