Abstract:
Existing visualization design and evaluation frameworks rest on a distinction between the designer and the user. However, there is little explicit guidance on design, analysis and evaluation when the designer is the user. A simple solution to this problem is for the researcher (who combines the designer and user roles) to be clear about which activity they are conducting at which point in time. To support the researcher, we propose a design, analysis and evaluation model. This model complements existing visualization design and evaluation frameworks. We have adopted this model in our ongoing research into uncertainty in crowdsourced crisis information.

Abstract:
Scatterplot is one of the most popular techniques for multi-dimensional data visualization. However, it is not always easy to effectively represent the multi-dimensional spaces by scatterplots. Various dimension reduction and scatterplot matrix (SPM) techniques have been presented to effectively represent them in a single display space. Also, various interactive techniques such as ``Rolling the Dice'' have been presented to assist the visual analytics processes. We discuss a technique to represent multi-dimensional spaces using multiple scatterplots like SPM-based techniques. Here, SPM-based techniques has a drawback that each of the scatterplots will displayed very small when all pairs of dimensions are equally displayed. On the other hand, our technique presented in this poster selectively displays a set of meaningful pairs of dimensions, instead of displaying all pairs of dimensions. It calculates scores of pairs of dimensions, and selects pre-defined number of pairs of dimensions. Then, it defines the distances or connectivity of the scatterplots generated from the selected pairs of dimensions, and calculates their ideal positions based on the distances or connectivity. Finally, it places the set of scatterplots by a rectangle packing algorithm referring their ideal positions. Consequently, the technique places similarly looking scatterplots closer in the display spaces. It is useful to understand the correlations among many dimensions.

Abstract:
We introduce an approach for the segmentation, visualization and tracking of regions of interest in large scale tensor field datasets generated by computational turbulent combustion simulations. We use canopy clustering followed by a K-means algorithm to partition and cluster the tensor field components. The resulting clusters are tracked through multiple timesteps. Interactive, hardware-accelerated volume renderings are generated using the cluster indices. Results on two rich datasets show this approach can assist in the visual analysis of combustion tensor fields.

Abstract:
We introduce the Mangrove Topological Data Structure (Mangrove TDS) framework for modeling simplicial complexes. It is based on a graph-based representation of the data structures, called mangroves, which ensures an extensible representation of a data structure for simplicial complexes. Mangroves can be easily customized for any modeling need, including the efficient representation of non-manifold shapes, and of those simplices, not directly encoded in a mangrove, that we call ghost simplices. We discuss here the properties of this framework, and current and future developments.

Abstract:
It is industrial practice to conduct Design Reviews of product models by means of Virtual Reality (VR) systems. Especially in the car industry CAVEs and Power Walls are mandatory tools to visualize the current state of product models (digital prototypes). However such systems usually display only the geometric product structure as it is imported from CAD systems. Other information which are of prime importance for the decision making are usually not visualized, e.g. requirements, documents, regulations, non-visual relations between parts and function etc. Such information have to be looked up in external systems, e.g. product data management systems or other repositories. In order to improve the utility and efficiency of VR-based Design Reviews, such media breaks should be prevented and all necessary data should be available in the immersive environement. The present work provides an explorative study on possible visualization techniques for combined graphical and textual information within a CAVE. 21 subjects were asked to arrange product structures of a given product in 3d space, using various 3d interaction techniques. The resulting product structures were analysed and grouped by means of a category system. The results vary among others in terms of the visualizations of the product structure, the relations between words and the product model and the use of depth. Future research questions are derived from the results which take user experience and perceptual issues into account

Abstract:
We introduce a method to cluster large image collections. We first rescale and convert images into gray scales. We then threshold these scales to obtain black pixels and compute descriptors of the configurations of these black pixels. Finally, we cluster images based on their descriptors. In contrast to raster clustering, which uses the entire pixel raster for distance computations, our application, which uses a small set of descriptors, can handle large image collections within reasonable time.

Abstract:
We have defined and implemented atomic and dimension-independent simplification operators on a graph-based representation of Morse complexes, and we have defined and implemented a multi-resolution model for Morse complexes built through such simplification operators.

Abstract:
Existing research suggests that individual personality differences can influence performance with visualizations. In addition to stable traits such as locus of control, research in psychology has found that temporary changes in affect (emotion) can significantly impact individual performance on cognitive tasks. We examine the relationship between fundamental visual judgement tasks and affect through a crowdsourced user study that combines affective-priming techniques from psychology with longstanding graphical perception experiments. Our results suggest that affective-priming can significantly influence accuracy in visual judgements, and that some chart types may be more affected than others.

Abstract:
MostPixelsEver Cluster Edition is an extension of the Processing programming environment that enables visualization in cluster-driven display environments without extensive knowledge of programming languages, graphics interfaces, or distributed computing. The work described here enables visual artists, humanities scholars and students, and even traditional programmers to create interactive visualizations in high-resolution distributed environments with simplicity. MostPixelsEver hides the inherent complexity of distributed environments by abstraction, and makes it possible to rapidly create visualizations on large displays.

Abstract:
Previously published algorithms to construct isosurfaces with sharp edges and corners require 'Hermite' data, the exact intersection points of grid edges and the isosurface and the exact gradients at those intersection points. We are interested in constructing iso-surfaces with sharp edge and corners from regular grid of scalar data without any information about intersection points or gradients at those intersection points. We decompose the problem into two parts: 1) Compute gradients at grid vertices from scalar data; 2) Compute an isosurface with sharp edges and vertices from a regular grid of scalar and gradient values. We focus on the second problem, computing an isosurface from scalar and gradient data. We also describe a method for visualizing and evaluating the accuracy of a reconstructiong of sharp features.

Abstract:
We present an analytics-based framework for simultaneous visualization of large surface data collections arising in clinical neuroimaging studies. Termed Informatics Visualization for Neuroimaging (INVIZIAN), this framework allows the visualization of both cortical surfaces characteristics and feature relatedness in unison. It also uses dimension reduction methods to derive new coordinate systems using a Jensen-Shannon divergence metric for positioning cortical surfaces in a metric space such that the proximity in location is proportional to neuroanatomical similarity. Feature data such as thickness and volume are colored on the cortical surfaces and used to display both subject-specific feature values and global trends within the population. Additionally, a query-based framework allows the neuroscience researcher to investigate probable correlations between neuroanatomical and subject patient attribute values such as age and diagnosis.

Abstract:
The Congressional Budget Office (CBO) is an agency of the federal government with about 240 employees that provides the U.S. Congress with timely, nonpartisan analysis of important budgetary and economic issues. Recently, CBO began producing static infographics to present its headline stories and to provide information to the Congress in different ways.

Abstract:
The structure of a set of high dimensional data objects (e.g. images, documents, molecules, genetic expressions, etc.) is notoriously difficult to visualize. In contrast, lower dimensional structures (esp. 3 or fewer dimensions) are natural to us and easy to visualize. A not unreasonable approach then is to explore one low dimensional visualization after another in the hope that together these will shed light on the higher dimensional structure. In our poster, we describe the graph theoretic structure recently proposed by Hurley and Oldford (2011) that represents low-dimensional spaces as graph nodes and transitions between spaces as edges. Of interest are walks along these graphs that reveal meaningful structure. If the nodes are two dimensional and edges exist, say, only between 2d spaces which share a variate, then the walk could be represented dynamically as a series of scatterplots, one transitioning into the next via a 3d rigid transformation. We demonstrate how these graphs are constructed and dynamically explored via our open source R package, RnavGraph.

Abstract:
Physical visualizations have been around for several decades and remained mostly unnoticed. They recently became popular in the form of data sculptures, due to a proliferation of data-driven artefacts produced by the art and design communities, and to a wider availability of rapid prototyping facilities such as fab labs. It has been recently suggested that such physical data representations are suitable for demonstrative, artistic or communicative purposes. But can physical visualizations also help carry out actual information visualization tasks? We describe the design of the first user study whose goals are to assess the efficiency of physical visualizations compared to on-screen visualizations with a focus on the challenges posed by 3D visualizations and to better understand how people use physical data representations to answer visual questions.

Abstract:
In recent years, the quantity of time series data generated in a wide variety of domains grown consistently. Thus, it is difficult for analysts to process and understand this overwhelming amount of data. In the specific case of time series data another problem arises: time series can be highly interrelated. This problem becomes even more challenging when a set of parameters influences the progression of a time series. However, while most visual analysis techniques support the analysis of short time periods, e.g. one day or one week, they fail to visualize large-scale time series, ranging over one year or more. In our approach we present a time series matrix visualization that tackles this problem. Its primary advantages are that it scales to a large number of time series with different start and end points and allows for the visual comparison / correlation analysis of a set of influencing factors. To evaluate our approach, we applied our technique to a real-world data set, showing the impact of local weather conditions on the efficiency of photovoltaic power plants.

Abstract:
The Citizen Engagement for Energy Efficient Communities project at Oak Ridge National Laboratory aims to help energy consumers visualize, better understand, and become more aware of their energy consumption in order to lower their consumption and reduce their carbon footprint. The easiest way for the greatest number of users to be able to access their consumption data is via an easy to use, easily accessible web-based application. Through a combination of HTML5, PHP, MYSQL, JSON, and Javascript we were able to create such an application and create compelling visualizations that combines user's energy consumption data, climate data, and the usage of a user's peers to help consumers better understand their own energy usage.

Abstract:
Many approaches have been proposed for the visualization of time series. The reduced line charts (small multiples for time series) and the more recent horizon graphs are two of these visualization techniques with benefits for visualizing multiple time series that we propose to unify, using a variant of the pan and zoom interaction on the y axis. We compare in a user study reduced line charts, horizon graphs, and our own contribution---progressive horizon graphs---for different tasks and numbers of concurrent time series using datasets with small variations. While recent work has compared horizon graphs with others visualization techniques and has made some recommendations on their usability, the real advantages of this technique are not clear. The results of our controlled user study show that progressive horizon graphs overcome these two visualization techniques when the number of charts increases.

Abstract:
Plasma-based particle accelerators can produce and sustain thousands of times stronger acceleration fields than conventional particle accelerators, providing a potential solution to the problem of the growing size and cost of conventional particle accelerators. There is a pressing need for computational methods that aid in scientific knowledge discovery from the ever growing collections of accelerator simulation data generated by accelerator physicists to investigate next-generation plasma-based particle accelerator designs. To address this challenge we describe in this poster a novel approach for automatic detection and classification of particle beams and beam substructures due to temporal differences in the acceleration process, here called acceleration features. By combining the automatic feature detection with a novel visualization tool for fast, intuitive, query-based exploration of acceleration features, we enable an effective top-down data exploration process, starting from a high-level, feature-based view down to the level of individual particles. We describe the application of our analysis in practice to study the formation and evolution of particle beams using simulations modeling different plasma-based accelerator designs.

Abstract:
We address the problem of visualizing demographic faultlines, a fundamental construct developed to understand dynamics among members in diverse workgroups. We propose a visual representation for this purpose that is based on multiple linked, stacked histograms in a parallel axis layout. In collaboration with management researchers, we evaluate our technique qualitatively using both synthetic data and real-world data of an empirical faultlines study.

Abstract:
Interactive visualization and analysis of large and complex volume data is still a big challenge. Compression-domain volume rendering methods have shown that mathematical tools to represent and com- press large data are very successful. We use a new framework that is widely used for data approximation and tensor approximation (TA). Specific properties of the TA bases are elaborated in the context of multiresolution and multiscale volume visualization.

Abstract:
We conducted a within-subject experiment involving 13 participants that empirically explore how two different models of story delivery involving information visualization influence audience-constructed narratives. The first model involves a speaker using visualization software to communicate a direct narrative, while the second involves constructing a story by interactively exploring visualization software. We used an open-ended questionnaire in controlled laboratory settings, with the primary goal of collecting a number of stories derived from the two models, followed by two Likert-scale questions on the ease of telling and curiosity about the story in each delivery model. We qualitatively analysed the stories constructed by the participants, based on a number of themes tied to storytelling, including time and place and narrative structure. The study's results reveal some interesting possible differences in how users receive, interpret, and create stories in each case.

Abstract:
Temporal awareness is pivotal to successful real-time dynamic decision making in a wide range of command and control situations; particularly in safety-critical environments. However, little explicit support for operators' temporal awareness is provided by decision support systems (DSS) for time-critical decisions. In the context of functional simulations of naval anti-air warfare and emergency response management, the present study compares operator support provided by two display formats. In both environments, we contrast a baseline condition to a condition in which a temporal display was integrated to the original interface to support operators' temporal awareness. We also wish to establish whether the implementation of time-based DSSs may also come with drawbacks on cognitive functioning and performance.

Abstract:
We work on the first step towards building a fiber bundle atlas on porcine hearts with diffusion tensor imaging (DTI) by (1) generating porcine heart fiber models from DTI data, (2) clustering the DTI fibers, and (3) matching cross-subject DTI fiber bundles. We also explore the effects of different distance thresholds on the DTI fiber bundle clustering and matching. Our results show that (1) complete-linkage leads to greater number of matches than single-linkage; (2) the smaller the minimum distance threshold is, the more matched bundles are obtained; (3) several fiber bundles are consistently matched across subjects with different sets of parameters.

Abstract:
The discrete nature of categorical data often confounds the direct application of existing multidimensional visualization techniques. To harness such discrete nature, we propose to utilize entropy related measures to enhance the visualization of categorical data. The entropy information is employed to guide the analysis, ordering, and filtering in visualizations of Scatter Plot Matrix and a variation of Parallel Sets.

Abstract:
Datasets that are collected for research often contain millions of records and may carry hidden pitfalls that are hard to detect. This work demonstrates how visual analytics can be used for identifying problems in the spatial distribution of crawled photographic data in different datasets: Picasa Web Albums, Panoramio, Flickr and Geograph, chosen to be potential data sources for ongoing doctoral research. This poster summary describes a number of problems found in the datasets using visual analytics and suggests that greater attention should be paid to assessing the quality of data gathered from user-generated photographic content. This work is the first part of a three-year PhD project aimed at producing a pedestrian-routing system that can suggest attractive pathways extracted from user-generated photographic content.

Abstract:
Modeling and simulating a brain's connectivity produces an immense amount of data, which has to be analyzed in a timely fashion. Neuroscientists are currently modeling parts of the brain e.g. the visual cortex of primates like Macaque monkeys in order to deduce functionality and transfer newly gained insights to the human brain. Current research leverages the power of today's High Performace Computing (HPC) machines in order to simulate low level neural activity. In this paper, we describe an interactive analysis tool that enables neuroscientists to visualize the resulting simulation output. One of the driving challenges behind our development is the integration of macroscopic data, e.g. brain areas, with microscopic simulation results, e.g. spiking behavior of individual neurons.

Abstract:
There is limited understanding of the relationship between neighbourhoods, demographic characteristics and domestic energy consumption habits. We report upon research that combines datasets relating to household energy use with geodemographics to enable better understanding of UK energy user types. A novel interactive interface is planned to evaluate the performance of specifically created energy-based data classifications. The research aims to help local governments and the energy industry in targeting households and populations for new energy saving schemes and in improving efforts to promote sustainable energy consumption. The new classifications may also stimulate consumption awareness amongst domestic users. This poster reports on initial visual findings and describes the research methodology, data sources and future visualisation requirements.

Abstract:
The visualization of clustered graphs is an essential tool for the analysis of networks, in which clustering techniques like community detection can reveal various structural properties. We give a brief description of how we succeeded to draw clustered graphs as topographic maps by combining a tree map approach with the topographic map metaphor. The proposed method is then applied to a similarity network of bestseller books of the Amazon website.

Abstract:
Mass and social media provide flows of images for real world events. It is sometimes difficult to represent realities and impressions of events using only text. However, even a single photo might remind us complex events. Along with events in the real world, there are representative images, such as design of products and commercial pictures. We can therefore recognize changes in trends of people's ideas, experiences, and interests through observing the flows of such representative images. This paper presents a novel 3D visualization system to explore temporal changes in trends using images associating with different topics, called Image Bricks. We show case studies using images extracted from our six-year blog archive. We first extract clusters of images as topics related to given keywords. We then visualize them on multiple timelines in a 3D space. Users can visually read stories of topics through exploring visualized images.

Abstract:
This poster presents an application of ContactTree, a new ego-centered visualization design, to charactering the collaborative activities of selected InfoVis researchers in terms of their publications as listed in DBLP. The ContactTree visualization, based on a botanical tree metaphor, is designed for studying individuals and comparing their social behavior and relationships with others based on data with rich attributes. As shown, the resulting trees give each of the selected researchers a distinct look, and many interesting patterns reveal themselves. In social science research, the study of people's social contacts and activities is of high interest. ContactTree visualization well complements conventional network visualization, which is better for showing relations among people and activities in a global context.