DevLan/vispubs huggingface dataset:
C-GROUP: A Visual Analytic Tool for Pairwise Analysis of Dynamic Group Membership
Authors: Hyunmo Kang, Lise Getoor, Lisa Singh
Published at VAST, 2007

C-GROUP is a tool for analyzing dynamic group membership in social networks over time. Unlike most network visualization tools, which show the group structure within an entire network, or the group membership for a single actor, C-GROUP allows users to focus their analysis on a pair of individuals of interest. And unlike most dynamic social network visualization tools, which focus on the addition and deletion of nodes (actors) and edges (relationships) over time, C-GROUP focuses on changing group memberships over time. C-GROUP provides users with a flexible interface for defining (and redefining) groups interactively, and allows users to view the changing group memberships for the pair over time. This helps to highlight the similarities and differences between the individuals and their evolving group memberships. C-GROUP allows users to dynamically select the time granularity of the temporal evolution and supports two novel visual representations of the evolving group memberships. This flexibility gives users alternate views that are appropriate for different network sizes and provides users with different insights into the grouping behavior.
ClusterSculptor: A Visual Analytics Tool for High-Dimensional Data
Authors: Eun Ju Nam, Yiping Han, Klaus Mueller 0001, Alla Zelenyuk, Dan Imre
Published at VAST, 2007

Cluster analysis (CA) is a powerful strategy for the exploration of high-dimensional data in the absence of a-priori hypotheses or data classification models, and the results of CA can then be used to form such models. But even though formal models and classification rules may not exist in these data exploration scenarios, domain scientists and experts generally have a vast amount of non-compiled knowledge and intuition that they can bring to bear in this effort. In CA, there are various popular mechanisms to generate the clusters, however, the results from their non- supervised deployment rarely fully agree with this expert knowledge and intuition. To this end, our paper describes a comprehensive and intuitive framework to aid scientists in the derivation of classification hierarchies in CA, using k-means as the overall clustering engine, but allowing them to tune its parameters interactively based on a non-distorted compact visual presentation of the inherent characteristics of the data in high- dimensional space. These include cluster geometry, composition, spatial relations to neighbors, and others. In essence, we provide all the tools necessary for a high-dimensional activity we call cluster sculpting, and the evolving hierarchy can then be viewed in a space-efficient radial dendrogram. We demonstrate our system in the context of the mining and classification of a large collection of millions of data items of aerosol mass spectra, but our framework readily applies to any high-dimensional CA scenario.
DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data
Authors: Niklas Elmqvist, John T. Stasko, Philippas Tsigas
Published at VAST, 2007

Supporting visual analytics of multiple large-scale multidimensional datasets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such datasets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a dataset displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to outsiders. Towards this end, the DataMeadow has a direct manipulation interface for selection, filtering, and creation of sets, subsets, and data dependencies using both simple and complex mouse gestures. We have evaluated our system using a qualitative expert review involving two researchers working in the area. Results from this review are favorable for our new method.
Design Considerations for Collaborative Visual Analytics
Authors: Jeffrey Heer, Maneesh Agrawala
Published at VAST, 2007

Information visualization leverages the human visual system to support the process of sensemaking, in which information is collected, organized, and analyzed to generate knowledge and inform action. Though most research to date assumes a single-user focus on perceptual and cognitive processes, in practice, sensemaking is often a social process involving parallelization of effort, discussion, and consensus building. This suggests that to fully support sensemaking, interactive visualization should also support social interaction. However, the most appropriate collaboration mechanisms for supporting this interaction are not immediately clear. In this article, we present design considerations for asynchronous collaboration in visual analysis environments, highlighting issues of work parallelization, communication, and social organization. These considerations provide a guide for the design and evaluation of collaborative visualization systems.
FemaRepViz: Automatic Extraction and Geo-Temporal Visualization of FEMA National Situation Updates
Authors: Chi-Chun Pan, Prasenjit Mitra
Published at VAST, 2007

An architecture for visualizing information extracted from text documents is proposed. In conformance with this architecture, a toolkit, FemaRepViz, has been implemented to extract and visualize temporal, geospatial, and summarized information from FEMA national update reports. Preliminary tests have shown satisfactory accuracy for FEMARepViz. A central component of the architecture is an entity extractor that extracts named entities like person names, location names, temporal references, etc. FEMARepViz is based on FactXtractor, an entity-extractor that works on text documents. The information extracted using FactXtractor is processed using GeoTagger, a geographical name disambiguation tool based on a novel clustering-based disambiguation algorithm. To extract relationships among entities, we propose a machine-learning based algorithm that uses a novel stripped dependency tree kernel. We illustrate and evaluate the usefulness of our system on the FEMA National Situation Updates. Daily reports are fetched by FEMARepViz from the FEMA website, segmented into coherent sections and each section is classified into one of several known incident types. We use concept Vista, Google maps and Google earth to visualize the events extracted from the text reports and allow the user to interactively filter the topics, locations, and time-periods of interest to create a visual analytics toolkit that is useful for rapid analysis of events reported in a large set of text documents.
Formalizing Analytical Discourse in Visual Analytics
Authors: Guoray Cai
Published at VAST, 2007

This paper presents a theory of analytical discourse and a formal model of the intentional structure of visual analytic reasoning process. Our model rests on the theory of collaborative discourse, and allows for cooperative human-machine communication in visual interactive dialogues. Using a sample discourse from a crisis management scenario, we demonstrated the utility of our theory in characterizing the discourse context and collaboration. In particular, we view analytical discourse as plans consisting of complex mental attitude towards analytical tasks and issues. Under this view, human reasoning and computational analysis become integral part of the collaborative plan that evolves through discourse.
From Tasks to Tools: A Field Study in Collaborative Visual Analytics
Authors: Daniel Ha, Minjung Kim, Andrew Wade, William Chao, Kevin I.-J. Ho, Linda T. Kaastra, Brian D. Fisher, John Dill
Published at VAST, 2007

This poster presents an exploratory field study of a VAST 2007 contest entry. We applied cognitive task analysis (CTA), grounded theory (GT), and activity theory (AT), to analysis of field notes and interviews from participants. Our results are described in the context of activity theory and sensemaking, two theoretical perspectives that we have found to be particularly useful in understanding analytic tasks.
IMAS: The Interactive Multigenomic Analysis System
Authors: Chris Shaw 0002, Greg A. Dasch, Marina E. Eremeeva
Published at VAST, 2007

This paper introduces a new Visual Analysis tool named IMAS (Interactive Multigenomic Analysis System), which combines common analysis tools such as Glimmer, BLAST, and Clustal-W into a unified Visual Analytic framework. IMAS displays the primary DNA sequence being analyzed by the biologist in a highly interactive, zoomable visual display. The user may analyze the sequence in a number of ways, and visualize these analyses in a coherent, sequence aligned form, with all related analysis products grouped together. This enables the user to rapidly perform analyses of DNA sequences without the need for tedious and error-prone cutting and pasting of sequence data from text files to and from web-based databases and data analysis services, as is now common practice.
InfoVis as Seen by the World Out There: 2007 in Review
Authors: Stephen Few
Published at VAST, 2007

How we as insiders see and understand InfoVis is quite different from how it is seen by most people in the world out there. Most people get only glimpses of what we do, and those glimpses rarely tell the story clearly. Think about the view of InfoVis that has been created in 2007 through marketing, blogs, and articles. This view is peppered with misperception. In this presentation, I'll take you on a tour of InfoVis' exposure in 2007: the highlights and the failures that have shaped the world's perception of our beloved and important work. The world needs what we do, but this need remains largely unsatisfied.
Intelligence Analysis Using Titan
Authors: Patricia Crossno, Brian N. Wylie, Andrew T. Wilson, John A. Greenfield, Eric T. Stanton, Timothy M. Shead, Lisa G. Ice, Kenneth Moreland, Jeffrey Baumes, Berk Geveci
Published at VAST, 2007

The open source Titan informatics toolkit project, which extends the visualization toolkit (VTK) to include information visualization capabilities, is being developed by Sandia National Laboratories in collaboration with Kitware. The VAST Contest provided us with an opportunity to explore various ideas for constructing an analysis tool, while allowing us to exercise our architecture in the solution of a complex problem. As amateur analysts, we found the experience both enlightening and fun.
Intelligent Visual Analytics Queries
Authors: Ming C. Hao, Umeshwar Dayal, Daniel A. Keim, D. Morent, Jörn Schneidewind
Published at VAST, 2007

Visualizations of large multi-dimensional data sets, occurring in scientific and commercial applications, often reveal interesting local patterns. Analysts want to identify the causes and impacts of these interesting areas, and they also want to search for similar patterns occurring elsewhere in the data set. In this paper we introduce the Intelligent Visual Analytics Query (IVQuery) concept that combines visual interaction with automated analytical methods to support analysts in discovering the special properties and relations of identified patterns. The idea of IVQuery is to interactively select focus areas in the visualization. Then, according to the characteristics of the selected areas, such as the data dimensions and records, IVQuery employs analytical methods to identify the relationships to other portions of the data set. Finally, IVQuery generates visual representations for analysts to view and refine the results. IVQuery has been applied successfully to different real-world data sets, such as data warehouse performance, product sales, and sever performance analysis, and demonstrates the benefits of this technique over traditional filtering and zooming techniques. The visual analytics query technique can be used with many different types of visual representation. In this paper we show how to use IVQuery with parallel coordinates, visual maps, and scatter plots.
Jigsaw meets Blue Iguanodon - The VAST 2007 Contest
Authors: Carsten Görg, Zhicheng Liu, Neel Parekh, Kanupriyah Singhal, John T. Stasko
Published at VAST, 2007

This article describes our use of the Jigsaw system in working on the VAST 2007 contest. Jigsaw provides multiple views of a document collection and the individual entities within those documents, with a particular focus on exposing connections between entities. We describe how we refined the identified entities in order to better facilitate Jigsaw's use and how the different views helped us to uncover key parts of the underlying plot.
Jigsaw: Supporting Investigative Analysis through Interactive Visualization
Authors: John T. Stasko, Carsten Görg, Zhicheng Liu, Kanupriya Singhal
Published at VAST, 2007

Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.
LAHVA: Linked Animal-Human Health Visual Analytics
Authors: Ross Maciejewski, Benjamin Tyner, Yun Jang, Cheng Zheng, Rimma V. Nehme, David S. Ebert, William S. Cleveland, Mourad Ouzzani, Shaun J. Grannis, Lawrence T. Glickman
Published at VAST, 2007

Coordinated animal-human health monitoring can provide an early warning system with fewer false alarms for naturally occurring disease outbreaks, as well as biological, chemical and environmental incidents. This monitoring requires the integration and analysis of multi-field, multi-scale and multi-source data sets. In order to better understand these data sets, models and measurements at different resolutions must be analyzed. To facilitate these investigations, we have created an application to provide a visual analytics framework for analyzing both human emergency room data and veterinary hospital data. Our integrated visual analytic tool links temporally varying geospatial visualization of animal and human patient health information with advanced statistical analysis of these multi-source data. Various statistical analysis techniques have been applied in conjunction with a spatio-temporal viewing window. Such an application provides researchers with the ability to visually search the data for clusters in both a statistical model view and a spatio-temporal view. Our interface provides a factor specification/filtering component to allow exploration of causal factors and spread patterns. In this paper, we will discuss the application of our linked animal-human visual analytics (LAHVA) tool to two specific case studies. The first case study is the effect of seasonal influenza and its correlation with different companion animals (e.g., cats, dogs) syndromes. Here we use data from the Indiana Network for Patient Care (INPC) and Banfield Pet Hospitals in an attempt to determine if there are correlations between respiratory syndromes representing the onset of seasonal influenza in humans and general respiratory syndromes in cats and dogs. Our second case study examines the effect of the release of industrial wastewater in a community through companion animal surveillance.
Literature Fingerprinting: A New Method for Visual Literary Analysis
Authors: Daniel A. Keim, Daniela Oelke
Published at VAST, 2007

In computer-based literary analysis different types of features are used to characterize a text. Usually, only a single feature value or vector is calculated for the whole text. In this paper, we combine automatic literature analysis methods with an effective visualization technique to analyze the behavior of the feature values across the text. For an interactive visual analysis, we calculate a sequence of feature values per text and present them to the user as a characteristic fingerprint. The feature values may be calculated on different hierarchy levels, allowing the analysis to be done on different resolution levels. A case study shows several successful applications of our new method to known literature problems and demonstrates the advantage of our new visual literature fingerprinting.
NewsLab: Exploratory Broadcast News Video Analysis
Authors: Mohammad Ghoniem, Dongning Luo, Jing Yang 0001, William Ribarsky
Published at VAST, 2007

In this paper, we introduce NewsLab, an exploratory visualization approach for the analysis of large scale broadcast news video collections containing many thousands of news stories over extended periods of time. A river metaphor is used to depict the thematic changes of the news over time. An interactive lens metaphor allows the playback of fine-grained video segments selected through the river overview. Multi-resolution navigation is supported via a hierarchical time structure as well as a hierarchical theme structure. Themes can be explored hierarchically according to their thematic structure, or in an unstructured fashion using various ranking criteria. A rich set of interactions such as filtering, drill-down/roll-up navigation, history animation, and keyword based search are also provided. Our case studies show how this set of tools can be used to find emerging topics in the news, compare different broadcasters, or mine the news for topics of interest.
Outlook for Visual Analytics Research Funding
Authors: James J. Thomas, Daniel A. Keim, Joe Kielman, Larry Rosenblum
Published at VAST, 2007

Visual Analytics has become a rapidly growing field of study. It is also a field that is addressing very significant real world problems in homeland security, business analytics, emergency management, genetics and bioinformatics, investigative analysis, medical analytics, and other areas. For both these reasons, it is attracting new funding and will continue to do so in the future. Visual analytics has also become an international field, with significant research efforts in Canada, Europe, and Australia, as well as the U.S. There is significant new research funding in Canada and Germany with other efforts being discussed, including a major program sponsored by the European Union. The contributors to this panel are some of the primary thought leaders providing research funding or involved in setting up the funding apparatus. We have asked them to present their needs, funding programs, and expectations from the research community. They all come from different perspectives, different missions, and different expectations. They will present their views of the range of activity in both the U.S. and internationally and discuss what is coming. Come learn about these programs, initiatives, and plans, and how you can contribute.
Point Placement by Phylogenetic Trees and its Application to Visual Analysis of Document Collections
Authors: Ana M. Cuadros, Fernando Vieira Paulovich, Rosane Minghim, Guilherme P. Telles
Published at VAST, 2007

The task of building effective representations to visualize and explore collections with moderate to large number of documents is hard. It depends on the evaluation of some distance measure among texts and also on the representation of such relationships in bi- dimensional spaces. In this paper we introduce an alternative approach for building visual maps of documents based on their content similarity, through reconstruction of phylogenetic trees. The tree is capable of representing relationships that allows the user to quickly recover information detected by the similarity metric. For a variety of text collections of different natures we show that we can achieve improved exploration capability and more clear visualization of relationships amongst documents.
Session Viewer: Visual Exploratory Analysis of Web Session Logs
Authors: Heidi Lam, Daniel M. Russell, Diane Tang, Tamara Munzner
Published at VAST, 2007

Large-scale session log analysis typically includes statistical methods and detailed log examinations. While both methods have merits, statistical methods can miss previously unknown sub- populations in the data and detailed analyses may have selection biases. We therefore built Session Viewer, a visualization tool to facilitate and bridge between statistical and detailed analyses. Taking a multiple-coordinated view approach, Session Viewer shows multiple session populations at the Aggregate, Multiple, and Detail data levels to support different analysis styles. To bridge between the statistical and the detailed analysis levels, Session Viewer provides fluid traversal between data levels and side-by-side comparison at all data levels. We describe an analysis of a large-scale web usage study to demonstrate the use of Session Viewer, where we quantified the importance of grouping sessions based on task type.
Situation Awareness Tool for Global Argus
Authors: Jae Choi, Sang-joon Lee, Sarah Gigitashvilli, James M. Wilson V
Published at VAST, 2007

We present a visualization tool to enhance situation awareness for Global Argus, a system that tracks and detects indications and warnings of biological events in near real time. Because Global Argus generates massive amounts of data daily, its analysts often struggle to interpret the information. To overcome this problem, we have developed the Global Argus situation awareness tool (GASAT) using the InteleView/World Wind geographical information system. This tool allows users to visualize current and past events in a particular region, and thus to understand how events evolve over time. Combined with the other tools that we are developing, GASAT will contribute to enhanced situation awareness in the tracking and detection of biological events.
Something's "Fishy" at Global Ways and Gill Breeders - Analysis with nSpace and GeoTime
Authors: Lynn Chien, Annie Tat, William Wright
Published at VAST, 2007

GeoTime and nSpace are two interactive visual analytics tools that support the process of analyzing massive and complex datasets. The two tools were used to examine and interpret the 2007 VAST contest dataset. This poster paper describes how the capabilities of the tools were used to facilitate and expedite every stage of an analyst workflow.
Spectra transformed for model-testing and visual exploration
Authors: Palmyra Catravas
Published at VAST, 2007

The presence of highly tangled patterns in spectra and other serial data exacerbates the difficulty of performing visual comparison between a test model for a particular pattern and the data. The use of a simple map that plants peaks in the data directly onto their corresponding position in a residual plot with respect to a chosen test model not only retrieves the advantages of dynamic regression plotting, but in practical cases also causes patterns in the data to congregate in meaningful ways with respect to more than one reference curve in the plane. The technique is demonstrated on a polyphonic music signal.