Interactive Visual Analysis of Multi-faceted Scientific Data
PhD THESIS, March 2011
Date of defense: May 27, 2011
Abstract
Visualization plays an important role in exploring, analyzing and presenting
large and heterogeneous scientific data that arise in many disciplines of
medicine, research, engineering, and others. We can see that model and data scenarios
are becoming increasingly multi-faceted: data are often multi-variate and
time-dependent, they stem from different data sources (multi-modal data), from
multiple simulation runs (multi-run data), or from multi-physics simulations of
interacting phenomena that consist of coupled simulation models (multi-model
data). The different data characteristics result in special challenges for visualization
research and interactive visual analysis. The data are usually large and
come on various types of grids with different resolution that need to be fused in
the visual analysis.
This thesis deals with different aspects of the interactive visual analysis of
multi-faceted scientific data. The main contributions of this thesis are: 1) a
number of novel approaches and strategies for the interactive visual analysis of
multi-run data; 2) a concept that enables the feature-based visual analysis across
an interface between interrelated parts of heterogeneous scientific data (including
data from multi-run and multi-physics simulations); 3) a model for visual analysis
that is based on the computation of traditional and robust estimates of statistical
moments from higher-dimensional multi-run data; 4) procedures for visual
exploration of time-dependent climate data that support the rapid generation
of promising hypotheses, which are subsequently evaluated with statistics; and
5) structured design guidelines for glyph-based 3D visualization of multi-variate
data together with a novel glyph. All these approaches are incorporated in a single
framework for interactive visual analysis that uses powerful concepts such as
coordinated multiple views, feature specification via brushing, and focus+context
visualization. Especially the data derivation mechanism of the framework has
proven to be very useful for analyzing different aspects of the data at different
stages of the visual analysis. The proposed concepts and methods are demonstrated
in a number of case studies that are based on multi-run climate data and
data from a multi-physics simulation.
Media
PhD thesis
Paper A
Hypothesis Generation in Climate Research with Interactive Visual Data Exploration
by J. Kehrer, F. Ladstädter, P. Muigg, H. Doleisch, A. Steiner, and H. Hauser. IEEE Transactions on
Visualization and Computer Graphics, 14(6):1579-1586, 2008.
Paper B
Critical Design and Realization Aspects of Glyph-based 3D Data Visualization
by A. E. Lie, J. Kehrer, and H. Hauser. In Proc. Spring Conference on Computer
Graphics (SCCG 2009), pages 27-34, 2009.
Paper C
Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface
by J. Kehrer, P. Muigg, H. Doleisch, and H. Hauser. IEEE Transactions on
Visualization and Computer Graphics, 17(7):934-946, 2011.
Paper D
Brushing Moments in Interactive Visual Analysis
by J. Kehrer, P. Filzmoser, and H. Hauser. Computer Graphics Forum,
29(3):813-822, 2010.
thesis
