2,222 research outputs found
Scale-Space Splatting: Reforming Spacetime for the Cross-Scale Exploration of Integral Measures in Molecular Dynamics
Understanding large amounts of spatiotemporal data from particle-based
simulations, such as molecular dynamics, often relies on the computation and
analysis of aggregate measures. These, however, by virtue of aggregation, hide
structural information about the space/time localization of the studied
phenomena. This leads to degenerate cases where the measures fail to capture
distinct behaviour. In order to drill into these aggregate values, we propose a
multi-scale visual exploration technique. Our novel representation, based on
partial domain aggregation, enables the construction of a continuous
scale-space for discrete datasets and the simultaneous exploration of scales in
both space and time. We link these two scale-spaces in a scale-space space-time
cube and model linked views as orthogonal slices through this cube, thus
enabling the rapid identification of spatio-temporal patterns at multiple
scales. To demonstrate the effectiveness of our approach, we showcase an
advanced exploration of a protein-ligand simulation.Comment: 11 pages, 9 figures, IEEE SciVis 201
The Emotional and Chromatic Layers of Urban Smells
People are able to detect up to 1 trillion odors. Yet, city planning is
concerned only with a few bad odors, mainly because odors are currently
captured only through complaints made by urban dwellers. To capture both good
and bad odors, we resort to a methodology that has been recently proposed and
relies on tagging information of geo-referenced pictures. In doing so for the
cities of London and Barcelona, this work makes three new contributions. We
study 1) how the urban smellscape changes in time and space; 2) which emotions
people share at places with specific smells; and 3) what is the color of a
smell, if it exists. Without social media data, insights about those three
aspects have been difficult to produce in the past, further delaying the
creation of urban restorative experiences.Comment: 11 pages, 18 figures, final version published in the Proceedings of
the Tenth International Conference on Web and Social Media (ICWSM 2016
Interactive Visual Analytics for Large-scale Particle Simulations
Particle based model simulations are widely used in scientific visualization. In cosmology, particles are used to simulate the evolution of dark matter in the universe. Clusters of particles (that have special statistical properties) are called halos. From a visualization point of view, halos are clusters of particles, each having a position, mass and velocity in three dimensional space, and they can be represented as point clouds that contain various structures of geometric interest such as filaments, membranes, satellite of points, clusters, and cluster of clusters.
The thesis investigates methods for interacting with large scale data-sets represented as point clouds. The work mostly aims at the interactive visualization of cosmological simulation based on large particle systems. The study consists of three components: a) two human factors experiments into the perceptual factors that make it possible to see features in point clouds; b) the design and implementation of a user interface making it possible to rapidly navigate through and visualize features in the point cloud, c) software development and integration to support visualization
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Occam's Razor and Petascale Visual Data Analysis
One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today?s largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena
Interactive 3D visualization for theoretical Virtual Observatories
Virtual Observatories (VOs) are online hubs of scientific knowledge. They
encompass a collection of platforms dedicated to the storage and dissemination
of astronomical data, from simple data archives to e-research platforms
offering advanced tools for data exploration and analysis. Whilst the more
mature platforms within VOs primarily serve the observational community, there
are also services fulfilling a similar role for theoretical data. Scientific
visualization can be an effective tool for analysis and exploration of datasets
made accessible through web platforms for theoretical data, which often contain
spatial dimensions and properties inherently suitable for visualization via
e.g. mock imaging in 2d or volume rendering in 3d. We analyze the current state
of 3d visualization for big theoretical astronomical datasets through
scientific web portals and virtual observatory services. We discuss some of the
challenges for interactive 3d visualization and how it can augment the workflow
of users in a virtual observatory context. Finally we showcase a lightweight
client-server visualization tool for particle-based datasets allowing
quantitative visualization via data filtering, highlighting two example use
cases within the Theoretical Astrophysical Observatory.Comment: 10 Pages, 13 Figures, Accepted for Publication in Monthly Notices of
the Royal Astronomical Societ
ScaleTrotter: Illustrative Visual Travels Across Negative Scales
We present ScaleTrotter, a conceptual framework for an interactive,
multi-scale visualization of biological mesoscale data and, specifically,
genome data. ScaleTrotter allows viewers to smoothly transition from the
nucleus of a cell to the atomistic composition of the DNA, while bridging
several orders of magnitude in scale. The challenges in creating an interactive
visualization of genome data are fundamentally different in several ways from
those in other domains like astronomy that require a multi-scale representation
as well. First, genome data has intertwined scale levels---the DNA is an
extremely long, connected molecule that manifests itself at all scale levels.
Second, elements of the DNA do not disappear as one zooms out---instead the
scale levels at which they are observed group these elements differently.
Third, we have detailed information and thus geometry for the entire dataset
and for all scale levels, posing a challenge for interactive visual
exploration. Finally, the conceptual scale levels for genome data are close in
scale space, requiring us to find ways to visually embed a smaller scale into a
coarser one. We address these challenges by creating a new multi-scale
visualization concept. We use a scale-dependent camera model that controls the
visual embedding of the scales into their respective parents, the rendering of
a subset of the scale hierarchy, and the location, size, and scope of the view.
In traversing the scales, ScaleTrotter is roaming between 2D and 3D visual
representations that are depicted in integrated visuals. We discuss,
specifically, how this form of multi-scale visualization follows from the
specific characteristics of the genome data and describe its implementation.
Finally, we discuss the implications of our work to the general illustrative
depiction of multi-scale data
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