11 research outputs found
Void-and-Cluster Sampling of Large Scattered Data and Trajectories
We propose a data reduction technique for scattered data based on statistical
sampling. Our void-and-cluster sampling technique finds a representative subset
that is optimally distributed in the spatial domain with respect to the blue
noise property. In addition, it can adapt to a given density function, which we
use to sample regions of high complexity in the multivariate value domain more
densely. Moreover, our sampling technique implicitly defines an ordering on the
samples that enables progressive data loading and a continuous level-of-detail
representation. We extend our technique to sample time-dependent trajectories,
for example pathlines in a time interval, using an efficient and iterative
approach. Furthermore, we introduce a local and continuous error measure to
quantify how well a set of samples represents the original dataset. We apply
this error measure during sampling to guide the number of samples that are
taken. Finally, we use this error measure and other quantities to evaluate the
quality, performance, and scalability of our algorithm.Comment: To appear in IEEE Transactions on Visualization and Computer Graphics
as a special issue from the proceedings of VIS 201
Visual Analysis of Multiple Dynamic Sensitivities along Ascending Trajectories in the Atmosphere
Numerical weather prediction models rely on parameterizations for
subgrid-scale processes, e.g., for cloud microphysics. These parameterizations
are a well-known source of uncertainty in weather forecasts that can be
quantified via algorithmic differentiation, which computes the sensitivities of
prognostic variables to changes in model parameters. It is particularly
interesting to use sensitivities to analyze the validity of physical
assumptions on which microphysical parameterizations in the numerical model
source code are based. In this article, we consider the use case of strongly
ascending trajectories, so-called warm conveyor belt trajectories, known to
have a significant impact on intense surface precipitation rates in
extratropical cyclones. We present visual analytics solutions to analyze
interactively the sensitivities of a selected prognostic variable, i.e. rain
mass density, to multiple model parameters along such trajectories. We propose
a visual interface that enables to a) compare the values of multiple
sensitivities at a single time step on multiple trajectories, b) assess the
spatio-temporal relationships between sensitivities and the shape and location
of trajectories, and c) a comparative analysis of the temporal development of
sensitivities along multiple trajectories. We demonstrate how our approach
enables atmospheric scientists to interactively analyze the uncertainty in the
microphysical parameterizations, and along the trajectories, with respect to a
selected prognostic variable. We apply our approach to the analysis of
convective trajectories within the extratropical cyclone "Vladiana", which
occurred between 22-25 September 2016 over the North Atlantic
Visuelle Analyse großer Partikeldaten
Partikelsimulationen sind eine bewährte und weit verbreitete numerische Methode in der Forschung und Technik. Beispielsweise werden Partikelsimulationen zur Erforschung der Kraftstoffzerstäubung in Flugzeugturbinen eingesetzt. Auch die Entstehung des Universums wird durch die Simulation von dunkler Materiepartikeln untersucht. Die hierbei produzierten Datenmengen sind immens. So enthalten aktuelle Simulationen Billionen von Partikeln, die sich über die Zeit bewegen und miteinander interagieren. Die Visualisierung bietet ein großes Potenzial zur Exploration, Validation und Analyse wissenschaftlicher Datensätze sowie der zugrundeliegenden
Modelle. Allerdings liegt der Fokus meist auf strukturierten Daten mit einer regulären Topologie. Im Gegensatz hierzu bewegen sich Partikel frei durch Raum und Zeit. Diese Betrachtungsweise ist aus der Physik als das lagrange Bezugssystem bekannt. Zwar können Partikel aus dem lagrangen in ein reguläres eulersches Bezugssystem, wie beispielsweise in ein uniformes Gitter, konvertiert werden. Dies ist bei einer großen Menge an Partikeln jedoch mit einem erheblichen Aufwand verbunden. Darüber hinaus führt diese Konversion meist zu einem Verlust der Präzision bei gleichzeitig erhöhtem Speicherverbrauch. Im Rahmen dieser Dissertation werde ich neue Visualisierungstechniken erforschen, welche speziell auf der lagrangen Sichtweise basieren. Diese ermöglichen eine effiziente und effektive visuelle Analyse großer Partikeldaten
Visualizing Big Data with augmented and virtual reality: challenges and research agenda
This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.publishedVersionPeer reviewe