@article{PB-VRVis-2016-010,
title = {Interactive Visual Analysis of Multi-Parameter Scientific
Data},
author = {Matkovic, Kresimir},
year = {2015},
month = {may},
abstract = {Increasing complexity and a large number of control
parameters make the design and understanding of modern
engineering systems impossible without simulation. Advances
in simulation technology and the ability to run multiple
simulations with different sets of parameters pose new
challenges for analysis techniques. The resulting data is
often heterogeneous. A single data point does not contain
scalars or vectors only, as usual. Instead, a single data
point contains scalars, time series, and other types of
mappings. Such a data model is common in many domains.
Interactive visual analysis utilizes a tight feedback loop
of computation/visualization and user interaction to
facilitate knowledge discovery in complex datasets. Our
research extends the visual analysis technology to
challenging heterogeneous data, in particular to a
combination of multivariate data and more complex data
types, such as functions, for example. Furthermore, we focus
on developing a structured model for interactive visual
analysis which supports a synergetic combination of user
interaction and computational analysis. The concept of
height surfaces and function graphs is a proven and well
developed mechanism for the analysis of a single mapping.
The state of the art when a set of such mappings is analyzed
suggested a use of different descriptors or aggregates in
the analysis. Our research makes it possible to analyze a
whole set of mappings (function graphs, or height surfaces,
for example) while keeping the original data. We advance
the interactive visual analysis to cope with complex
scientific data. Most of the analysis techniques consider
the data as a static source. Such an approach often hinders
the analysis. We introduce a concept of interactive visual
steering for simulation ensembles. We link the data
generation and data exploration and analysis tasks in a
single workflow. This makes it possible to tune and optimize
complex systems having high dimensional parameter space and
complex outputs.},
URL = {http://www.vrvis.at/publications/PB-VRVis-2016-007},
}