14 - 19 OCTOBER, 2012. SEATTLE, WASHINGTON, USA

2012 VIS Posters

Visweek Poster Viewing
A Case Study: Tracking and Visualizing the Evolution of Dark Matter Halos and Groups of Satellite Halos in Cosmology Simulations
Authors:
Jay Takle, Deborah Silver, Katrin Heitmann
Abstract:
In this poster, we track the evolution of cosmic structures and higher level host structures in cosmological simulation as they interact with each other. The structures found in these simulations are made up of groups of dark matter tracer particles called satellite halos and groups of satellite halos called host halos. We implement a multi-level tracking model to track dark matter tracer particles, satellite halos and host halos to understand their behaviour and show how the different structures are formed over time. We also represent the evolution of halos in the form of merger trees for detailed analysis by cosmologists.
A Design, Analysis and Evaluation Model to Support the Visualization Designer-User
Authors:
Iain Dillingham, Jason Dykes, Jo Wood
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.
A Generic Model for the Integration of Interactive Visualization and Statistical Computing Using R
Authors:
Johannes Kehrer, Roland N. Boubela, Peter Filzmoser, Harald Piringer
Abstract:
This poster describes general concepts of integrating the statistical computation package R into a coordinated multiple views framework. The integration is based on a cyclic analysis workflow. In this model, interactive selections are a key aspect to trigger and control computations in R. Dynamic updates of data columns are a generic mechanism to transfer computational results back to the interactive visualization. Further aspects include the integration of the R console and an R object browser as views in our system. We illustrate our approach by means of an interactive modeling process.
A Multi-dimensional Data Visualization Applying a Scatterplot Packing Technique
Authors:
Zheng Yunzhu, Haruka Suematsu, Takayuki Itoh, Ryohei Fujimaki, Satoshi Morinaga, Yoshinobu Kawahara
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.
A New Radial Space-Filling Visualization Approach for Planar st-Graphs
Authors:
Ilir Jusufi, Andreas Kerren, Yuanmao Yuanmao
Abstract:
Planar st-graphs are used in a number of different application fields in the sciences, but also in industry. So far, mainly node-link-based layouts have been used to visualize such graphs especially in the Graph Drawing community. One drawback of these standard layouts is their high consumption of space. In Information Visualization, there exist visualization techniques for graphs which achieve considerable space savings, such as matrix-based approaches. In this work, we present a novel space-filling representation to visualize planar st-graphs.
A Novel Method for Tracking Tensor-based Regions of Interest in Large-Scale, Spatially-Dense Turbulent Combustion Data
Authors:
Timothy Luciani, Adrian Maries, Hoang Tran, Levent Yilmaz, Mehdi Nik
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.
A visual analytics approach to understanding cycling behaviour
Authors:
Roger Beecham, Jo Wood, Audrey Bowerman
Abstract:
Existing research into cycling behaviours has either relied on detailed ethnographic studies or larger public attitude surveys. Instead, following recent contributions from information visualization and data mining, this design study uses visual analytics techniques to identify, describe and explain cycling behaviours within a large and attribute rich transactional dataset. Using data from London's bike share scheme, customer level classifications will be created, which consider the regularity of scheme use, journey length and travel times. Monitoring customer usage over time, user classifications will attend to the dynamics of cycling behaviour, asking substantive questions about how behaviours change under varying conditions. The 3-year PhD project will contribute to academic and strategic discussions around sustainable travel policy. A programme of research is outlined, along with an early visual analytics prototype for rapidly querying customer journeys.
An Extensible Framework for Modeling Simplicial Complexes
Authors:
David Canino, Leila De Floriani
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.
Animated Transitions and Navigation in Dynamic Networks
Authors:
Benjamin Bach, Emmanuel Pietriga, Jean-Daniel Fekete
Abstract:
We present GraphDiaries, an interface for temporal navigation in dynamic networks with changing topology. Navigation in time is necessary for the exploration of single network states at different points in time and to explore higher level changes over periods of time. GraphDiaries enhances conventional node link diagrams with staged animated transitions between any time steps and small multiples organized along a timeline, as well as different layout adaption techniques. GraphDiaries is designed as an extension to other graph visualization systems and is independent from particular layout algorithms and other visualization techniques.
Arrangement of Product Data in CAVE systems
Authors:
Elisabeth Dittrich, Johann Habakuk Israel
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
Augmenting Visual Representation of Affectively Charged Information using Sound Graphs
Authors:
Nadya A. Calderon, Bernhard E. Riecke, Brian Fisher
Abstract:
Within the Visual Analytics research agenda there is an interest on studying the applicability of multimodal information representation and interaction techniques for the analytical reasoning process. The present study summarizes a pilot experiment conducted to understand the effects of augmenting visualizations of affectively-charged information using auditory graphs. We designed an audiovisual representation of social comments made to different news posted on a popular website, and their affective dimension using a sentiment analysis tool for short texts. Participants of the study were asked to create an assessment of the affective valence trend (positive or negative) of the news articles using for it, the visualizations and sonifications. The conditions were tested looking for speed/accuracy trade off comparing the visual representation with an audiovisual one. We discuss our preliminary findings regarding the design of augmented information-representation.
Clustering Large Image Collections through Pixel Descriptors
Authors:
Tuan Dang, Leland Wilkinson
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.
Comparing Interactive Web-Based Visualization Rendering Techniques
Authors:
Daniel E. Kee, Liz Salowitz, Remco Chang
Abstract:
As the need to display complex visualizations on the web has increased, a wide variety of rendering techniques have emerged. However, no single implementation has become standard, leaving developers with many options to choose from and no clear way of selecting the best one for their problem. We implemented a 2D parallel coordinates visualization, using a variety of common ren- dering techniques, and empirically tested them for rendering speed and interactivity on increasingly large data sets. The HTML5 can- vas with a WebGL context performed best although the developer cost was highest. The performance requirements of a project may dictate a given implementation, but some options clearly do better than others, even for relatively large amounts of data.
Dimension-Independent Simplification and Multi-Resolution Representation of Morse Complexes
Authors:
Lidija Comic, Leila De Floriani, Federico Iuricich
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.
Exploring Flow Fields Using Fractal Analysis of Field Lines
Authors:
Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, Marc Khoury, Rephael Wenger
Abstract:
We present a novel technique for analyzing the geometry of streamlines representing large scale flow fields produced in scientific simulations. We introduce the box counting ratio, a metric related to the Kolmogorov capacity or box counting dimension, for quantifying geometric complexity of streamlines (or streamline segments). We utilize this metric to drive a visual analytic framework for extracting, organizing and representing features of varying sizes from large number of streamlines. This framework allows the user to easily visualize and interact with the features otherwise hidden in large data. We present case studies using combustion and climate simulation datasets.
Exploring the Impact of Emotion on Visual Judgement
Authors:
Lane Harrison, Remco Chang, Aidong Lu
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.
Exploring Vector Fields with Distribution-based Streamline Analysis
Authors:
Kewei Lu, Abon Chaudhuri, Teng-Yok Lee, Alexander G. Suttmiller, Han-Wei Shen, Pak Chung Wong
Abstract:
Streamline-based techniques are designed based on the idea that properties of streamlines are indicative of features in the underlying field. In this paper, we show that statistical distributions of measurements along the trajectory of a streamline can be used as a robust and effective descriptor to measure the similarity between streamlines. With the distribution-based approach, we present a framework for interactive exploration of 3D vector fields with streamline query and clustering. We demonstrate the utility of our framework with simulation data sets of varying nature and size.
Extending the Processing Programming Environment to Tiled Displays
Authors:
Brandt Westing, Robert Turknett
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.
Facettice: Integrating Faceted Navigation and Concept Lattices for Visual Data Exploration
Authors:
Benjamin Bach, Jan Polowinski, Dietrich Kammer
Abstract:
We present a novel interface for the exploration of multivariate data by integrating faceted search and interactive concept lattice visualizations. Concept lattices are Hasse diagrams showing hierarchical sub-concept relations in multidimensional data and result from a preceding Formal Concept Analysis. A concept lattice can be interpreted as the navigation space of exactly all possible results for a search that is based on conjunctive queries between terms. With Facettice we describe the two complementary visualizations Facet Lattices and Big Smart Lattice supporting interactive faceted search while visualizing and navigating in the concept lattice space. The design of our two visualizations is based on three integration goals which describe how faceted search can benefit from concept lattice visualizations and vice versa.
Feature Sensitive Isosurface Extraction from Gradient Data
Authors:
Arindam Bhattacharya, Rephael Wenger
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.
Feature-Enhanced Map for 2D Multivariate Data with Uncertainty
Authors:
Keqin Wu, Song Zhang
Abstract:
We present our method to solve the challenge of visualizing multivariate data with uncertainty in a single image. First, layers of spots enhanced with contour patterns are designed to encode multivariate information. Second, star glyphs are adapted to encode uncertainty information with each branch representing the uncertainty of an individual variable. Layers of sparse and similar glyphs allow for an easy and accurate interpretation of uncertainty. Distinct shapes and colors of spots and stars reduce the interference between the individual variables and their uncertainties. This feature enhancement allows the major features of individual variables to stand out in a sea of information.
Feature-Similarity Visualization of MRI Cortical Surface Data
Authors:
Ian Bowman, Shantanu Joshi, Vaughan Greer, Jack Van Horn
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.
Incorporating GOMS Analysis into the Design of an EEG Data Visual Analysis Tool
Authors:
Hua Guo, Diem Tran, David Laidlaw
Abstract:
In this paper, we present a case study where we incorporate GOMS (Goals, Operators, Methods, and Selectors) task analysis into the design process of a visual analysis tool. We performed GOMS analysis on an Electroencephalography (EEG) analyst's current data analysis strategy to identify important user tasks and unnecessary user actions in his current workflow. We then designed an EEG data visual analysis tool based on the GOMS analysis result. Evaluation results show that the tool we have developed, EEGVis, allows the user to analyze EEG data with reduced subjective cognitive load, faster speed and increased confidence in the analysis quality. The positive evaluation results suggest that our design process demonstrates an effective application of GOMS analysis to discover opportunities for designing better tools to support the user's visual analysis process.
Infographics at the Congressional Budget Office
Authors:
Jonathan Schwabish
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.
Information Retrieval Failure Analysis: Visual Analytics as a Support for Interactive 'What-If' Investigation
Authors:
Marco Angelini, Nicola Ferro, Guido Granato, Giuseppe Santucci, Gianmaria Silvello
Abstract:
This poster provides an analytical model for examining performances of IR systems, based on the discounted cumulative gain family of metrics, and visualization for interacting and exploring the performances of the system under examination. Moreover, we propose machine learning approach to learn the ranking model of the examined system in order to be able to conduct a 'what-if' analysis and visually explore what can happen if you adopt a given solution before having to actually implement it.
Interactive Visual Clustering of High Dimensional Data by Exploring Low-Dimensional Subspaces
Authors:
Adrian Waddell, R. Wayne Oldford
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.
Interactive Word Cloud Rendering with Semantic Zooming
Authors:
Xiaotong Liu, Kang-Che Lee, Teng-Yok Lee, Han-Wei Shen
Abstract:
We present an interactive technique that can render aliasing-free and coherent word clouds. Words that are not legible are first identified and appropriately adjusted in size to remove aliasing. The adjusted words are either placed in a local optimal position without overlapping the existing words, or filtered out for effective utilization of screen space. The layout of the refined word cloud is then compressed by a novel force-directed model. We demonstrate the effectiveness and usefulness of our technique with an application in semantic zooming of word clouds.
Investigating Physical Visualizations
Authors:
Yvonne Jansen, Pierre Dragicevic, Jean-Daniel Fekete
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.
LensingWikipedia: Parsing Text for the Interactive Visualization of Human History
Authors:
Ravikiran Vadlapudi, Maryam Siahbani, Anoop Sarkar, John Dill
Abstract:
Extracting information from text is challenging. Most current practices treat text as a bag of words or word clusters, ignoring valuable linguistic information. Leveraging this linguistic information, we propose a novel approach to visualize textual information. The novelty lies in using state-of-the-art Natural Language Processing (NLP) tools to automatically annotate text which provides a basis for new and powerful interactive visualizations. Using NLP tools, we built a web-based interactive visual browser for human history articles from Wikipedia.
Matrix-Based Visual Correlation Analysis on Large Timeseries Data
Authors:
Michael Behrisch, James Davey, Tobias Schreck, Daniel Keim, Jarn Kohlhammer
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.
Multiscale interactive visualization: concrete achievements
Authors:
Debora Testi, Gordon Clapworthy, Stephen Aylward, Xavier Planes, Richard Christie
Abstract:
Integrative research, involving the modeling of living systems at different scales, is becoming more extensively used in the biomedical community. For this reason, an open-source library, called MSVTK, is being implemented to fill the gap in software visualization solutions handling multiscale data. The library adopts state-of-the-art visualization and interaction techniques to solve the various challenges. The efficacy of the proposed software solutions is being demonstrated on exemplary problems collected from current biomedical research studies.
OD Maps for Studying Historical Internal Migration in Ireland
Authors:
Aidan Slingsby, Mary Kelly, Jason Dykes, Jo Wood
Optimizing an SPT-Tree for Visual Analytics
Authors:
Connor Gramazio, Remco Chang
Abstract:
Despite the extensive work done in the scientific visualization community on the creation and optimization of spatial data structures, there has been little adaptation of these structures in visual analytics and information visualization. In this work we present how we modify a space-partioning time (SPT) tree -- a structure normally used in direct-volume rendering -- for geospatial-temporal visualizations. We also present optimization techniques to improve the traversal speed of our structure through locational codes and bitwise comparisons. Finally, we present the results of an experiment that quantitatively evaluates our modified SPT tree with and without our optimizations. Our results indicate that retrieval was nearly three times faster when using our optimizations, and are consistent across multiple trials. Our finding could have implications for performance in using our modified SPT tree in large-scale geospatial temporal visual analytics software.
Practical Web Based Visualization for Comparative Energy Usage Analysis
Authors:
Christopher Maness, Chad Steed, Olufemi Omitaomu
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.
Priming Locus of Control to Affect Performance
Authors:
Alvitta Ottley, R. Jordan Crouser, Caroline Ziemkiewicz, Remco Chang
Abstract:
Recent research suggests that the personality trait Locus of Control(LOC) can be a reliable predictor of performance when learning a new visualization tool. While these results are compelling and have direct implications to visualization design, the relationship between a user's LOC measure and their performance is not well understood. We hypothesize that there is a dependent relationship between LOC and performance; specifically, a person's orientation on the LOC scale directly influences their performance when learning new visualizations. To test this hypothesis, we conduct an experiment with 300 subjects using Amazon's Mechanical Turk. We adapt techniques from personality psychology to manipulate a user's LOC so that users are either primed to be more internally or externally oriented on the LOC scale. Replicating previous studies investigating the effect of LOC on performance, we measure users' speed and accuracy as they use visualizations with varying visual metaphors. Our findings demonstrate that changing a user's LOC impacts their performance. We find that a change in users' LOC results in performance changes.
Progressive Horizon Graphs: Improving Small Multiples Visualization of Time Series
Authors:
Charles Perin, Frederic Vernier, Jean-Daniel Fekete
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.
ProxiViz: an Interactive Visualization Technique to Overcome Multidimensional Scaling Artifacts
Authors:
Nicolas Heulot, Michael Aupetit, Jean-Daniel Fekete
Abstract:
Projection algorithms such as multidimensional scaling are often used to visualize high-dimensional data. However, when attempting to interpret the visualization of the resulting 2D projection, users are faced with artifacts. This poster introduces ProxiViz: an interactive technique to provide better insights about the original data-space. Primary results of a controlled experiment show that ProxiViz is significantly more effective than the baseline projection techniques for a visual clustering task.
Query-driven Analysis of Plasma-based Particle Acceleration Data
Authors:
Oliver Rubel, Cameron G.R. Geddes, Min Chen, Estelle Cormier-Michel, E. Wes Bethel
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.
Recent Advances in the Equalizer Parallel Rendering Framework
Authors:
Stefan Eilemann
Abstract:
In this poster we present the recent advances in Equalizer, a framework for scalable parallel rendering based on OpenGL, which provides an application programming interface (API) to develop scalable graphics applications for a wide range of systems ranging from large distributed visualization clusters and multi-processor multi-GPU graphics systems to single-GPU desktop machines. Recent advances include optimizations for visualization clusters using multi-GPU NUMA nodes, tile and subpixel decompositions, automatic configuration on multi-GPU machines and scalable visualization clusters, as well as novel features for Virtual Reality, most notably support for dynamic focus distance.
Show Me the Cracks in Our Teams: Visual Representations of Demographic Diversity Faultlines
Authors:
Tuan Pham, Ronald Metoyer, Katerina Bezrukova, Chester Spell
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.
SketchPad N-D: An Interface for High-Dimensional Dataset Generation and Editing
Authors:
Puripant Ruchikachorn, Bing Wang, Klaus Mueller
Abstract:
In order to generate data with known and desired features for high-dimensional data testing, we propose a tool that allows users to generate multivariate data directly within the same interface they would also use to visualize the data. We demonstrate our ideas with two well-established visualization paradigms, one based on the parallel coordinate framework, the other based on scatterplots.
Tensor Approximation Properties for Multiresolution and Multiscale Volume Visualization
Authors:
Susanne K. Suter, Renato Pajarola
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.
The Beatles Genome Project: Cluster Analysis and Visualization of Popular Music
Authors:
Douglas Mason
Abstract:
We present a database system for storing and retrieving abstracted musical information as well as visualizations for interpreting this information. We then apply hierarchical cluster analysis to show statistical phenomena occuring in a corpus of popular songs written by the Beatles. We find that chords and melodic rhythms fall into distinct clusters which distinguish each song, indicating possible principles of composition.
The Effect of Information Visualization Delivery on Narrative Construction and Development
Authors:
Donia Badawood, Jo Wood
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.
The spatiotemporal multivariate hypercube for discovery of patterns in event data
Authors:
Fred Olislagers, Marcel Worring
Abstract:
Event data can hold valuable decision making information, yet detecting interesting patterns in this type of data is not an easy task because the data is usually rich and contains spatial, temporal as well as multivariate dimensions. Research into visual analytics tools to support the discovery of patterns in event data often focuses on the spatiotemporal or spatiomultivariate dimension of the data only. Few research efforts focus on all three dimensions in one framework. An integral view on all three dimensions is, however, required to unlock the full potential of event datasets. In this poster, we present an event visualization, transition, and interaction framework that enables an integral view on all dimensions of spatiotemporal multivariate event data. The framework is built around the notion that the event data space can be considered a spatiotemporal multivariate hypercube. Results of a case study we performed suggest that a visual analytics tool based on the proposed framework is indeed capable to support users in the discovery of multidimensional spatiotemporal multivariate patterns in event data.
Time-Oriented Visualization and Anticipation
Authors:
Cindy Chamberland, Francois Vachon, Jean-Francois Gagnon, Simon Banbury, Sebastien Tremblay
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.
Toward Composable Interactive Visualizations
Authors:
Karl Smeltzer, Martin Erwig, Ronald Metoyer, Christophe Torne
Abstract:
While existing toolkits offer users a means for creating custom data visualizations, building complex, interactive systems remains a tedious task. The most notable shortcoming is the inability to compose interactive visualization components and marks into a cohesive whole without a great deal of rework to scale and place marks manually. We introduce CIViL, a domain specific language for creating composable and interactive information visualizations. Focusing on concise representations and consistent semantics, CIViL allows users to specify interactive visualizations by composing simple visual components. By leveraging declarative principles, users are able to specify visualizations in terms of both the visual elements and the behavior that they desire, without needing to consider how the visualization will be generated, scaled, or displayed. We present a prototype implementation in Haskell along with several examples to demonstrate the potential of the language.
Towards Constructing a Fiber Bundle Atlas on Porcine Hearts with Diffusion Tensor Imaging
Authors:
Ruiyi Wu, Song Zhang, Allen Crow
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.
Towards Visual Sedimentation
Authors:
Samuel Huron, Romain Vuillemot, Jean Daniel Fekete
Abstract:
We present Visual Sedimentation, a new design metaphor for visualizing streaming data inspired by the geological process of sedimentation. Our work started by early experiments visualizing political Twitter streams during the French 2012 presidential elections, and social interactions during a TV show. In both cases, the positive feedback we received expressed an unexpectedly high level of engagement from users, guiding our generalization of the metaphor. This article explores Visual Sedimentation and describes a new generative design space for Information Visualization. Geological sedimentation is our inspiration as it smoothly aggregates falling objects by compacting them into strata. We use this idea to visualize changing information in a new way by providing continuity between the representation of new and older data. The metaphor preserves an overall visual encoding while making it suitable for monitoring streaming data generated at unpredictable rates.
Using Entropy in Enhancing Visualization of High Dimensional Categorical Data
Authors:
Jamal Alsakran, Ye Zhao, Xiaoke Huang, Alex Midget, Jing Yang
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.
Using Translational Science in Visual Analytics
Authors:
Tera Marie Green, Brian Fisher
Abstract:
We introduce translational science, a research discipline from medicine, and show how adapting it for visual analytics can improve the design and evaluation of visual analytics interfaces. Translational science 'translates' knowledge from the lab to the real-world to 'ground truth' by incorporating a 3 phase program of research. Phase 1 & 2 include protocols for research in the lab and field and Phase 3 focuses on dissemination and documentation. We discuss these phases and how they may be applied to visual analytics research.
Using Visual Analytics to Detect Problems in Datasets Collected From Photo-Sharing Services
Authors:
Alexander Kachkaev, Jo Wood
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.
VDQAM: A Toolkit for Database Quality Evaluation based on Visual Morphology
Authors:
Dongxing Teng, Haiyan Yang, Cuixia Ma, Hongan Wang
Abstract:
Data quality evaluation is one of the most critical steps during the data mining processes. Data with poor quality often leads to poor performance in data mining, low efficiency in data analysis, wrong decision which bring great economic loss to users and organizations further. Although many researches have been carried out from various aspects of the extracting, transforming, and loading processes in data mining, most researches pay more attention to analysis automation than to data quality evaluation. To address the data quality evaluation issues, we propose an approach to combine human beings' powerful cognitive abilities in data quality evaluation with the high efficiency ability of computer, and develop a visual analysis method for data quality evaluation based on visual morphology.
VisNEST - Interactive Analysis of Neural Activity Data
Authors:
Christian Nowke, Bernd Hentschel, Torsten Kuhlen, Jochen Eppler, Sacha van Albada, Rembrandt Bakker, Markus Diesmann, Maximilian Schmidt
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.
Visual Exploration of Local Interest Points in Sets of Time Series
Authors:
Tobias Schreck, Lyubka Sharalieva, Franz Wanner, Juergen Bernard, Tobias Ruppert, Tatiana von Landesberger, Benjamin Bustos
Abstract:
Visual analysis of time series data is an important, yet challenging task with many application examples in fields such as financial or news stream data analysis. Many visual time series analysis approaches consider a global perspective on the time series. Fewer approaches consider visual analysis of local patterns in time series, and often rely on interactive specification of the local area of interest. We present initial results of an approach that is based on automatic detection of local interest points. We follow an overview-first approach to find useful parameters for the interest point detection, and details-on-demand to relate the found patterns. We present initial results and detail possible extensions of the approach.
Visualising Variations in Household Energy Consumption
Authors:
Sarah Goodwin, Jason Dykes
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.
Visualization Technique of Gene Network and Ontology Applying Edge Bundling
Authors:
Rina Nakazawa, Takayuki Itoh, Jun Sese, Aika Terada
Abstract:
Gene networks have constructed with genes as nodes, and interactions between genes as edges so as to reveal unknown gene functions and relationships. Since nodes and edges of gene networks are usually very numerous, it may be difficult to understand relations between genomic functions and gene-gene interactions, if it is visualized by traditional techniques. This poster presents our technique on visualization of gene networks and gene ontology (GO), which summarizes gene functions and attributes. The technique represents the functions defined by GO terms as colors of nodes, and bundles edges depending on the gene functions to ease visual complication of the network.
Visualizing Book Similarity as Topographic Map
Authors:
Martin Gronemann, Michael Junger
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.
Visualizing Cyber Physical Data Streams Using Radial Pixel Rings
Authors:
Ming Hao, Manish Marwah, Sebastian Mittelstudt, Halldur Janetzko, Daniel Keim, Umeshwar Dayal, Cullen Bash, Carlos Felix, Chandrakant Patel, Meichun Hsu
Abstract:
Cyber physical systems (CPS), such as smart buildings and data centers, are richly instrumented systems composed of tightly coupled computational and physical elements that generate large amounts of data. To explore CPS data and obtain actionable insights, we construct a Radial Pixel Visualization (RPV) system, which uses multiple concentric rings to show the data in a compact circular layout of small polygons (pixel cells), each of which represents an individual data value. RPV provides an effective visual representation of locality and periodicity of the high volume, multivariate data streams, and seamlessly combines them with the results of an automated analysis. In the outermost ring the results of correlation analysis and peak point detection are highlighted. Our explorations demonstrates how RPV can help administrators to identify periodic thermal hot spots, understand data center energy consumption, and optimize IT workload.
Visualizing Flows of Images in Social Media
Authors:
Masahiko Itoh, Masashi Toyoda, Tetsuya Kamijo, Masaru Kitsuregawa
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.
Visualizing Flu Pandemic for Model Validation
Authors:
Karla Vega, Kelly Gaither, Francesca Samsel, Gregory P. Johnson, Nedialko Dimitrov, Lauren Ancel Meyers
Abstract:
This poster describes the design methodology and results for a pan- demic visualization tool that was developed for validating a pan- demic model. This work is the result of a collaboration between domain scientists, visualization scientists and a fine artist. While the primary goal of this work is to understand and communicate how an epidemic spreads across a country, we developed new visu- alization methods for population center glyphs and travel networks. The visualization tool was developed using Processing and uses ge- ographical spatiotemporal views to explore the model dataset. The tool is interactive and easy to use. Close attention was given to visualization design aesthetics to create visualizations that can be easily understood by the general public.
Visualizing InfoVis Researchers using ContactTrees
Authors:
Arnaud Sallaberry, Kwn-Liu Ma
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.
Whole-Brain Vascular Reconstruction, Simulation, and Visualization
Authors:
Thomas Marrinan, Ian Gould, Chih-Yang Hsu, Andreas Linninger
Abstract:
Current techniques in medical imaging and analysis primarily focus on recording information about one specific physiological property at a time. Various modalities such as magnetic resonance, computed tomography, and digital subtraction angiography are each suited towards different tasks. In order to improve surgical planning, physicians would benefit from patient- specific computational models built from medical images. These models could be used in order to run simulations and simultaneously gather physiological information that would otherwise require multiple imaging modalities or be impossible to measure with current technology. We present a pipeline for processing medical data and executing computational simulations to enhance the information conveyed in standard medical imaging. Our work focuses on the whole brain, where we've developed tools that allow vasculature to be analyzed in three-dimensions, at high resolutions, and with multiple relevant data sets overlaid on the vascular structure. In order to avoid confusion and misinterpretations, we have the ability to render simulated data such that it mirrors raw medical images and vascular reconstructions.
Symposia: BioVis & LDAV
Date & Time :  Monday, October 15 08:30 am - 05:55 pm
Location : Ballroom C, Ballroom D, & 2nd Floor Hallway
Posters : 
Symposia: BioVis & LDAV
Abstract:

BioVis and LDAV 2012 promise two full days of exciting talks on cutting-edge research. To view the full program, please visit http://biovis.net/biovis/2012/papers-by-session and http://ldav.org/.

BioVis and LDAV posters can be viewed from 8:30am - 5:55pm in the 2nd Floor Hallway.

Date & Time :  Sunday, October 14 08:30 am - 05:55 pm
Location : Grand Ballroom C, Grand Ballroom D, & 2nd Floor Hallway
Posters : 
Symposia: BioVis & LDAV
Abstract:

BioVis and LDAV 2012 promise two full days of exciting talks on cutting-edge research. To view the full program, please visit http://biovis.net/biovis/2012/papers-by-session and http://ldav.org/.

BioVis will be held in Grand Ballroom C. 

LDAV will be held in Grand Ballroom D.

BioVis and LDAV posters can be viewed from 8:30am - 5:55pm in the 2nd Floor Hallway.