IEEE VIS Publication Dataset

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This article presents SpiralView, a visualization tool for helping system administrators to assess network policies. The tool is meant to be a complementary support to the routine activity of network monitoring, enabling a retrospective view on the alarms generated during and extended period of time. The tool permits to reason about how alarms distribute over time and how they correlate with network resources (e.g., users, IPs, applications, etc.), supporting the analysts in understanding how the network evolves and thus in devising new security policies for the future. The spiral visualization plots alarms in time, and, coupled with interactive bar charts and a users/applications graph view, is used to present network data and perform queries. The user is able to segment the data in meaningful subsets, zoom on specific related information, and inspect for relationships between alarms, users, and applications. In designing the visualizations and their interaction, and through tests with security experts, several ameliorations over the standard techniques have been provided.

A story is a powerful abstraction used by intelligence analysts to conceptualize threats and understand patterns as part of the analytical process. This paper demonstrates a system that detects geo-temporal patterns and integrates story narration to increase analytic sense-making cohesion in GeoTime. The GeoTime geo-temporal event visualization tool was augmented with a story system that uses narratives, hypertext linked visualizations, visual annotations, and pattern detection to create an environment for analytic exploration and communication, thereby assisting the analyst in identifying, extracting, arranging and presenting stories within the data The story system lets analysts operate at the story level with higher-level abstractions of data, such as behaviors and events, while staying connected to the evidence. The story system was developed and evaluated in collaboration with analysts.

Computational and experimental sciences produce and collect ever- larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In this paper, we introduce Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and the largest data volume supernova search currently in operation. Sunfall utilizes novel interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. Sunfall is currently in operation at the Nearby Supernova Factory; it is the first visual analytics system in production use at a major astrophysics project.

TexPlorer is an integrated system for exploring and analyzing large amounts of text documents. The data processing modules of TexPlorer consist of named entity extraction, entity relation extraction, hierarchical clustering, and text summarization tools. Using a timeline tool, tree-view, table-view, and concept maps, TexPlorer provides an analytical interface for exploring a set of text documents from different perspectives and allows users to explore vast amount of text documents efficiently.

We have developed a Web 2.0 thin client visualization framework called GeoBoosttrade. Our framework focuses on geospatial visualization and using scalable vector graphics (SVG), AJAX, RSS and GeoRSS we have built a complete thin client component set. Our component set provides a rich user experience that is completely browser based. It includes maps, standard business charts, graphs, and time-oriented components. The components are live, interactive, linked, and support real time collaboration.