Interactive Visual Analysis of Multi-faceted Scientific Data

Abstract

<p>Visualization plays an important role in exploring, analyzing and presentinglarge and heterogeneous scientific data that arise in many disciplines ofmedicine, research, engineering, and others.  We can see that model and data scenariosare becoming increasingly <i>multi-faceted</i>:  data are often multi-variate andtime-dependent, they stem from different data sources (multi-modal data), frommultiple simulation runs (multi-run data), or from multi-physics simulations ofinteracting phenomena that consist of coupled simulation models (multi-modeldata).  The different data characteristics result in special challenges for visualizationresearch and interactive visual analysis.  The data are usually large andcome on various types of grids with different resolution that need to be fused inthe visual analysis.</p><p>This thesis deals with different aspects of the interactive visual analysis ofmulti-faceted scientific data.  The main contributions of this thesis are:  1) anumber of novel approaches and strategies for the interactive visual analysis ofmulti-run data;  2) a concept that enables the feature-based visual analysis acrossan interface between interrelated parts of heterogeneous scientific data (includingdata from multi-run and multi-physics simulations);  3) a model for visual analysisthat is based on the computation of traditional and robust estimates of statisticalmoments from higher-dimensional multi-run data;  4) procedures for visualexploration of time-dependent climate data that support the rapid generationof promising hypotheses, which are subsequently evaluated with statistics;  and5) structured design guidelines for glyph-based 3D visualization of multi-variatedata together with a novel glyph.  All these approaches are incorporated in a singleframework for interactive visual analysis that uses powerful concepts such ascoordinated multiple views, feature specification via brushing, and focus+contextvisualization.  Especially the data derivation mechanism of the framework hasproven to be very useful for analyzing different aspects of the data at differentstages of the visual analysis.  The proposed concepts and methods are demonstratedin a number of case studies that are based on multi-run climate data anddata from a multi-physics simulation.</p>