Browsing by Author "Schulz, Christoph"

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Open Access
Analyse der Oberflächenstruktur von Proteinen
(2011) Schulz, Christoph
Diese Diplomarbeit befasst sich mit dem Thema Analyse der Oberflächenstruktur von Proteinen auf moderner Grafikhardware. Das Ziel der Diplomarbeit war es die technische Machbarkeit des interaktiven Umgangs mit zeitabhängigen Datensätzen aus Molekulardynamik-Simulationen zu zeigen. Es wurde ein Verfahren entwickelt, um aus Volumendaten ein Dreiecksnetz zu extrahieren, Komponenten zu erkennen, diese über Zeit zu korrelieren und Ereignisse mit zeitlichem Bezug, wie Amalgamieren und Aufspalten, zu erkennen. Die Implementierung erfolgte mittels NVIDIAs CUDA-Technologie. Anschließend wurde das Leistungsverhalten des Verfahrens untersucht und so der Beweis erbracht, dass der interaktive Umgang mit zeitabhängigen Proteindaten machbar ist.
Open Access
Uncertainty-aware visualization techniques
(2021) Schulz, Christoph; Weiskopf, Daniel (Prof. Dr.)
Nearly all information is uncertainty-afflicted. Whether and how we present this uncertainty can have a major impact on how our audience perceives such information. Still, uncertainty is rarely visualized and communicated. One difficulty is that we tend to interpret illustrations as truthful. For example, it is difficult to understand that a drawn point’s presence, absence, and location may not convey its full information. Similarly, it may be challenging to classify a point within a probability distribution. One must learn how to interpret uncertainty-afflicted information. Accordingly, this thesis addresses three research questions: How can we identify and reason about uncertainty? What are approaches to modeling flow of uncertainty through the visualization pipeline? Which methods are suitable for harnessing uncertainty? The first chapter is concerned with sources of uncertainty. Then, approaches to model uncertainty using descriptive statistics and unsupervised learning are discussed. Also, a model for validation and evaluation of visualization methods is proposed. Further, methods for visualizing uncertainty-afflicted networks, trees, point data, sequences, and time series are presented. The focus lies on modeling, propagation, and visualization of uncertainty. As encodings of uncertainty, we propose wave-like splines and sampling-based transparency. As an overarching approach to adapt existing visualization methods for uncertain information, we identify the layout process (the placement of objects). The main difficulty is that these objects are not simple points but distribution functions or convex hulls. We also develop two stippling-based methods for rendering that utilize the ability of the human visual system to cope with uncertainty. Finally, I provide insight into possible directions for future research.
Open Access
Visual analytics of multivariate intensive care time series data
(2022) Brich, N.; Schulz, Christoph; Peter, J.; Klingert, W.; Schenk, M.; Weiskopf, Daniel; Krone, M.
We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.