The conflux of two growing areas of technologycollaboration and visualization-into a new research direction, collaborative visualization, provides new research challenges. Technology now allows us to easily connect and collaborate with one another-in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Digital information is now regularly accessed by multiple people in order to share information, to view it together, to analyze it, or to form decisions. Visualizations are used to deal more effectively with large amounts of information while interactive visualizations allow users to explore the underlying data. While researchers face many challenges in collaboration and in visualization, the emergence of collaborative visualization poses additional challenges but is also an exciting opportunity to reach new audiences and applications for visualization tools and techniques.The purpose of this article is (1) to provide a definition, clear scope, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported cooperative work (CSCW) and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. We conclude by discussing a research agenda for future work on collaborative visualization and urge for a new generation of visualization tools that are designed with collaboration in mind from their very inception.

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As scientific instruments and computer simulations produce more and more data, the task of locating the essential information to gain insight becomes increasingly difficult. FastBit is an efficient software tool to address this challenge. In this article, we present a summary of the key techniques, namely bitmap compression, encoding and binning. The advances in these techniques have led to a search tool that can answer structured (SQL) queries orders of magnitude faster than popular database systems. To illustrate how FastBit is used in applications, we present three examples involving a high-energy physics experiment, a combustion simulation, and an accelerator simulation. In each case, FastBit significantly reduces the response time and enables interactive exploration on terabytes of data.

This paper presents a new technique for detecting sharp features on point-sampled geometry. Sharp features of different nature and possessing angles varying from obtuse to acute can be identified without any user interaction. The algorithm works directly on the point cloud, no surface reconstruction is needed. Given an unstructured point cloud, our method first computes a Gauss map clustering on local neighborhoods in order to discard all points which are unlikely to belong to a sharp feature. As usual, a global sensitivity parameter is used in this stage. In a second stage, the remaining feature candidates undergo a more precise iterative selection process. Central to our method is the automatic computation of an adaptive sensitivity parameter, increasing significantly the reliability and making the identification more robust in the presence of obtuse and acute angles. The algorithm is fast and does not depend on the sampling resolution, since it is based on a local neighbor graph computation.

The recently introduced notion of Finite-Time Lyapunov Exponent to characterize Coherent Lagrangian Structures provides a powerful framework for the visualization and analysis of complex technical flows. Its definition is simple and intuitive, and it has a deep theoretical foundation. While the application of this approach seems straightforward in theory, the associated computational cost is essentially prohibitive. Due to the Lagrangian nature of this technique, a huge number of particle paths must be computed to fill the space-time flow domain. In this paper, we propose a novel scheme for the adaptive computation of FTLE fields in two and three dimensions that significantly reduces the number of required particle paths. Furthermore, for three-dimensional flows, we show on several examples that meaningful results can be obtained by restricting the analysis to a well-chosen plane intersecting the flow domain. Finally, we examine some of the visualization aspects of FTLE-based methods and introduce several new variations that help in the analysis of specific aspects of a flow.

This paper presents the state of the art in the area of topology-based visualization. It describes the process and results of an extensive annotation for generating a definition and terminology for the field. The terminology enabled a typology for topological models which is used to organize research results and the state of the art. Our report discusses relations among topological models and for each model describes research results for the computation, simplification, visualization, and application. The paper identifies themes common to subfields, current frontiers, and unexplored territory in this research area.

One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.

The paper presents a topology-based visualization method for time-dependent two-dimensional vector elds. A time interpolation enables the accurate tracking of critical points and closed orbits as well as the detection and identication of structural changes. This completely characterizes the topology of the unsteady ow. Bifurcation theory provides the theoretical framework. The results are conveyed by surfaces that separate subvolumes of uniform ow behavior in a three-dimensional space-time domain.