IEEE VIS Publication Dataset

This abstract presents a bricolage approach to the 2007 VAST contest. The analytical process we used is presented across four stages of sensemaking. Several tools were used throughout our approach, and we present their strengths and weaknesses for specific aspects of the analytical process. In addition, we review the details of both individual and collaborative techniques for solving visual analytics problems.

This abstract presents a bricolage approach to the 2007 VAST contest. The analytical process we used is presented across four stages of sensemaking. Several tools were used throughout our approach, and we present their strengths and weaknesses for specific aspects of the analytical process. In addition, we review the details of both individual and collaborative techniques for solving visual analytics problems.

Wikipedia is a wiki-based encyclopedia that has become one of the most popular collaborative on-line knowledge systems. As in any large collaborative system, as Wikipedia has grown, conflicts and coordination costs have increased dramatically. Visual analytic tools provide a mechanism for addressing these issues by enabling users to more quickly and effectively make sense of the status of a collaborative environment. In this paper we describe a model for identifying patterns of conflicts in Wikipedia articles. The model relies on users' editing history and the relationships between user edits, especially revisions that void previous edits, known as "reverts". Based on this model, we constructed Revert Graph, a tool that visualizes the overall conflict patterns between groups of users. It enables visual analysis of opinion groups and rapid interactive exploration of those relationships via detail drill- downs. We present user patterns and case studies that show the effectiveness of these techniques, and discuss how they could generalize to other systems.

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 paper describes how the capabilities of the tools were used to facilitate and expedite every stage of the analysis.

Visual analytics experts realize that one effective way to push the field forward and to develop metrics for measuring the performance of various visual analytics components is to hold an annual competition. The second visual analytics science and technology (VAST) contest was held in conjunction with the 2007 IEEE VAST symposium. In this contest participants were to use visual analytic tools to explore a large heterogeneous data collection to construct a scenario and find evidence buried in the data of illegal and terrorist activities that were occurring. A synthetic data set was made available as well as tasks. In this paper we describe some of the advances we have made from the first competition held in 2006.

ATS Intelligent Discovery analyzed the VAST 2007 contest data set using two of its proprietary applications, NdCore and REGGAE (Relationship Generating Graph Analysis Engine). The paper describes these tools and how they were used to discover the contest's scenarios of wildlife law enforcement, endangered species issues, and ecoterrorism.

ATS intelligent discovery analyzed the VAST 2007 contest data set using two of its proprietary applications, NdCore and REGGAE (relationship generating graph analysis engine). The paper describes these tools and how they were used to discover the contest's scenarios of wildlife law enforcement, endangered species issues, and ecoterrorism.

TexPlorer is an integrated system for exploring and analyzing vast amount of text documents. The data processing modules of TexPlorer consist of named entity extraction, entity relation extraction, hierarchical clustering, and text summarization tools. Using time line tool, tree-view, table-view, and concept maps, TexPlorer provides visualizations from different aspects and allows analysts to explore vast amount of text documents efficiently.

The investigation of the VAST Contest collection provided a valuable test for text mining techniques. Our group has focused on creating analytical tools to unveil relevant patterns and to aid with the content navigation in such text collections. Our results show how such an approach, in combination with visualization techniques, can ease the discovery process especially when multiple tools founded on the same approach to data mining are used in complement to and in concert with one another.

The investigation of the VAST Contest collection provided a valuable test for text mining techniques. Our group has focused on creating analytical tools to unveil relevant patterns and to aid with the content navigation in such text collections. Our results show how such an approach, in combination with visualization techniques, can ease the discovery process especially when multiple tools founded on the same approach to data mining are used in complement to and in concert with one another.

We present a new framework - VisPad - to support the user to revisit the visual exploration process, and to synthesize and disseminate information. It offers three integrated views. The data view allows the user to interactively explore the data. The navigation view captures the exploration process. It enables the user to revisit any particular state and reuse it. The knowledge view enables the user to record his/her findings and the relations between these findings.

Wikipedia is a large and rapidly growing Web-based collaborative authoring environment, where anyone on the Internet can create, modify, and delete pages about encyclopedic topics. A remarkable property of some Wikipedia pages is that they are written by up to thousands of authors who may have contradicting opinions. In this paper we show that a visual analysis of the "who revises whom"- network gives deep insight into controversies. We propose a set of analysis and visualization techniques that reveal the dominant authors of a page, the roles they play, and the alters they confront. Thereby we provide tools to understand how Wikipedia authors collaborate in the presence of controversy.

Many dynamic networks have associated geological information. Here we present two complementing visual analysis methods for such networks. The first one provides an overview with summerized information while the second one presents a more detailed view. The geological information is encoded in the network layout, which is designed to help maintain user's mental map. We also combined visualization with social network analysis to facilitate knowledge discovery, especially to understand network changes in the context overall evolution. Both methods are applied to the "History of the FIFA World Cup Competition" data set.

Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster- affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool results and direct its work. A transportation schedule, which is generated by the tool, is a complex construct involving geographical space, time, and heterogeneous objects (people and vehicles) with states and positions varying in time. We apply task-analytical approach to design techniques that could effectively support a human planner in the analysis of this complex information H. 1.2 [User/Machine Systems]: Human information processing - Visual Analytics; 1.6.9 [Visualization]: information visualization.

Using mobile devices for visualization provides a ubiquitous environment for accessing information and effective decision making. These visualizations are critical in satisfying the knowledge needs of operators in areas as diverse as education, business, law enforcement, protective services, medical services, scientific discovery, and homeland security. In this paper, we present an efficient and interactive mobile visual analytic system for increased situational awareness and decision making in emergency response and training situations. Our system provides visual analytics with locational scene data within a simple interface tailored to mobile device capabilities. In particular, we focus on processing and displaying sensor network data for first responders. To verify our system, we have used simulated data of The Station nightclub fire evacuation.

This article briefly introduces the Jigsaw system and describes how we used it in analysis activities for the VAST '07 Contest. Jigsaw is a visual analytic system that provides multiple coordinated views to show connections between entities that are extracted from a collection of documents.

Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships among keywords and accounts over time. Furthermore, we introduce a search-by-example technique which extracts accounts that show similar transaction patterns. In collaboration with the Anti-Money Laundering division at Bank of America, we demonstrate that using our tool, investigators are able to detect accounts and transactions that exhibit suspicious behaviors.

We present a powerful framework for 3D-texture-based rendering of multiple arbitrarily intersecting volumetric datasets. Each volume is represented by a multi-resolution octree-based structure and we use out-of-core techniques to support extremely large volumes. Users define a set of convex polyhedral volume lenses, which may be associated with one or more volumetric datasets. The volumes or the lenses can be interactively moved around while the region inside each lens is rendered using interactively defined multi-volume shaders. Our rendering pipeline splits each lens into multiple convex regions such that each region is homogenous and contains a fixed number of volumes. Each such region is further split by the brick boundaries of the associated octree representations. The resulting puzzle of lens fragments is sorted in front-to-back or back-to-front order using a combination of a view-dependent octree traversal and a GPU-based depth peeling technique. Our current implementation uses slice-based volume rendering and allows interactive roaming through multiple intersecting multi-gigabyte volumes.

We present a general framework for the modeling and optimization of scalable multi-projector displays. Based on this framework, we derive algorithms that can robustly optimize the visual quality of an arbitrary combination of projectors without manual adjustment. When the projectors are tiled, we show that our framework automatically produces blending maps that outperform state-of-the-art projector blending methods. When all the projectors are superimposed, the framework can produce high-resolution images beyond the Nyquist resolution limits of component projectors. When a combination of tiled and superimposed projectors are deployed, the same framework harnesses the best features of both tiled and superimposed multi-projector projection paradigms. The framework creates for the first time a new unified paradigm that is agnostic to a particular configuration of projectors yet robustly optimizes for the brightness, contrast, and resolution of that configuration. In addition, we demonstrate that our algorithms support high resolution video at real-time interactive frame rates achieved on commodity graphics platforms. This work allows for inexpensive, compelling, flexible, and robust large scale visualization systems to be built and deployed very efficiently.

Conveying shape using feature lines is an important visualization tool in visual computing. The existing feature lines (e.g., ridges, valleys, silhouettes, suggestive contours, etc.) are solely determined by local geometry properties (e.g., normals and curvatures) as well as the view position. This paper is strongly inspired by the observation in human vision and perception that a sudden change in the luminance plays a critical role to faithfully represent and recover the 3D information. In particular, we adopt the edge detection techniques in image processing for 3D shape visualization and present photic extremum lines (PELs) which emphasize significant variations of illumination over 3D surfaces. Comparing with the existing feature lines, PELs are more flexible and offer users more freedom to achieve desirable visualization effects. In addition, the user can easily control the shape visualization by changing the light position, the number of light sources, and choosing various light models. We compare PELs with the existing approaches and demonstrate that PEL is a flexible and effective tool to illustrate 3D surface and volume for visual computing.