This paper discusses algorithmic and implementation issues of optimally mapping a visualization pipeline onto a linear arrangement of wide-area network nodes to minimize the total delay. The first network node typically is a data source, the last node could be a display device ranging from a personal computer to a powerwall, and each intermediate node could be a workstation or computational cluster. This mapping scheme appropriately distributes the filtering, geometry generation, rendering, and display modules of the visualization pipeline among various network nodes to make efficient use of the computing resources at end nodes and also the network bandwidth between them. We present an analytical formulation of this problem by taking into account the computational speeds of nodes, bandwidths between them, and the sizes of messages exchanged between the visualization modules. We present polynomial-time optimal algorithms using the dynamic programming method to compute the mappings with minimum total delays for two cases. We implemented an OpenGL-based remote visualization system and deployed it at three geographically distributed nodes. By utilizing bandwidth estimation modules, we implemented and tested the proposed mapping scheme to evaluate both the network transport and computational performance.

Data repositories around the world hold many thousands of datasets. A problem for remote dataset users is to browse the repositories and efficiently locate relevant datasets. In this note, we introduce the Iconic Remote Visual Data Exploration tool (IRVDX), which provides visual browsing for exploring the features of remote and distributed data without the necessity of downloading the entire dataset. IRVDX employs three kinds of visualizations: one provides a reduced representation of the datasets, which we call Dataset Icons. These icons show the important statistical characteristics of datasets and help to identify relevant datasets from distributed repositories. Another one is called the Remote Dataset Visual Browser that provides visualizations to browse remote data without downloading the complete dataset to identify its content. The final one provides visualizations to show the degree of similarity between two datasets and to visually determine whether a join of two remote datasets will be meaningful. In this paper, we describe the design and implementation of IRVDX in detail. We assess the benefits of our Dataset Icons against the traditional text-based interfaces and show the usefulness of IRVDX by conducting experiments with datasets from the UCI KDD Archive.

Self-Organizing map (SOM) is a widely used tool to find clustering and also to visualize high dimensional data. Several spherical SOMs have been proposed to create a more accurate representation of the data by removing the “border effect”. In this paper, we compare several spherical lattices for the purpose of implementation of a SOM. We then introduce a 2D rectangular grid data structure for representing the geodesic dome. This new approach improves the neighborhood searching process in the spherical gird. The new Geodesic SOM and its data structure are tested using socio-demographic data. In the experiments, we try to create a notion of direction in the Geodesic SOM. The direction facilitates more consistent visual comparison of different datasets as well as to assist viewers building their mental maps.

Datasets from Computational Fluid Dynamics (CFD) can be post-processed and visualized to aid understanding of the flow phenomena present. Visualization of CFD data, however, often suffers from problems such as occlusion and cluttering when methods such as glyphing and volume rendering are applied. In this paper we present a case study where new modes for haptic interaction are used to enhance the exploration of CFD data. A VR environment with interactive graphics and an integrated graphical user interface has been implemented. In contrast to previous work on haptic interaction with CFD data we employ a 'reactive' haptic scheme as opposed to direct force maping. The reactive approach not only generates more stable feedback but also provides clearer and more intuitive cues about the underlying data. Two haptic modes are used to enhance the understanding of different features in the flow data: One presents the orientation of the data and also guides the user to follow the stream as it flows around the aircraft fuselage. The other provides a haptic representation of vortex data. This mode enables the user to perceive and so follow tendencies of vorticity and vortices.

The paper presents an illustrative visualization technique that provides expressive representations of large-scale 3D city models, inspired by the tradition of artistic and cartographic visualizations typically found in bird’s-eye view maps and panoramic maps. First, a collection of city model components is defined. Secondly, a real-time multi-pass rendering algorithm is described that achieves comprehensible, abstract 3D city model depictions based on edge enhancement, color-based and shadow-based depth cues, and procedural facade texturing. The illustrative visualization provides an effective visual interface to urban spatial information and associated thematic information in a way that is complementary to visual interfaces based on the Virtual Reality paradigm, offering a huge potential for graphics design. Primary application areas include city and landscape planning, cartoon worlds in computer games, and tourist information systems.

Recent data mining techniques exploit patterns of statistical independence in multivariate data to make conjectures about cause/effect relationships. These relationships can be used to construct causal graphs, which are sometimes represented by weighted node-link diagrams, with nodes representing variables and combinations of weighted links and/or nodes showing the strength of causal relationships. We present an interactive visualization for causal graphs (ICGs), inspired in part by the Influence Explorer. The key principles of this visualization are as follows: Variables are represented with vertical bars attached to nodes in a graph. Direct manipulation of variables is achieved by sliding a variable value up and down, which reveals causality by producing instantaneous change in causally and/or probabilistically linked variables. This direct manipulation technique gives users the impression they are causally influencing the variables linked to the one they are manipulating. In this context, we demonstrate the subtle distinction between seeing and setting of variable values, and in an extended example, show how this visualization can help a user understand the relationships in a large variable set, and with some intuitions about the domain and a few basic concepts, quickly detect bugs in causal models constructed from these data mining techniques.

Visual display of information in data mining can support successful knowledge discovery. An experiment was conducted to identify parameters that affect the detection of cause-and-effect relations in time series data in a visual data mining environment. Accuracy of performance and the frequency of tool usage were measured as a function of visual properties of the cause-function and information processing styles. Performance accuracy differed between participants with different cognitive styles. Participants with high analytic cognitive style were better able to detect cause-and-effect relations through the investigation of visual and more global properties of the displayed data. Visual properties of the data affected users with high analytic and low experiential cognitive styles similarly and had no direct effect on accuracy. Participants with different levels of cognitive style differed in tool usage, indicating diverse approaches to solving the experimental task. The results point to the need to consider the effects of user characteristics and properties of the displayed data when designing visual data mining environments that are based on intense interaction of users with complex graphical displays.

A common approach to analyze geo-related data is using bar charts or x-y plots. They are intuitive and easy to use. But important information often gets lost. In this paper, we introduce a new interactive visualization technique called Geo Pixel Bar Charts, which combines the advantages of Pixel Bar Charts and interactive maps. This technique allows analysts to visualize large amounts of spatial data without aggregation and shows the geographical regions corresponding to the spatial data attribute at the same time. In this paper, we apply Geo Pixel Bar Charts to visually mining sales transactions and Internet usage from different locations. Our experimental results show the effectiveness of this technique for providing data distribution and immediate identification of anomalies from the map.

Interval volume serves as a generalized isosurface and represents a three-dimensional subvolume for which the associated scalar filed values lie within a user-specified closed interval. In general, it is not an easy task for novices to specify the scalar field interval corresponding to their ROIs. In order to extract interval volumes from which desirable geometric features can be mined effectively, we propose a suggestive technique which extracts interval volumes automatically based on the global examination of the field contrast structure. Also proposed here is a simplification scheme for decimating resultant triangle patches to realize efficient transmission and rendition of large-scale interval volumes. Color distributions as well as geometric features are taken into account to select best edges to be collapsed. In addition, when a user wants to selectively display and analyze the original dataset, the simplified dataset is restructured to original quality. These two proposed methods can also be used for batch processing. Several simulated and acquired datasets are used to demonstrate the effects of the methods.

The Interval Volume Decomposer (IVD) is an interface for decomposing an entire volume into interval volumes each of which characterizes a distinctive volume feature. The advantage of the IVD is that it allows us to look inside the volume by peeling interval volumes from outside to inside not only interactively but also automatically. This is achieved due to the rigorous analysis of nested structures of the decomposed interval volumes by constructing a level-set graph that delineates isosurface transitions according to the scalar field. A robust algorithm for computing such level-set graphs is introduced in order to extract significant structures in the volume by putting together local interval volumes into a finite number of global groups. Several decomposition examples of medical and simulated datasets are demonstrated so that the present interface effectively traverses the underlying structures of the volume.

Over the last 18 months, our team at the Laboratory for Computational Science & Engineering (LCSE) at the University of Minnesota has been moving our data analysis and visualization applications from small clusters of PCs within our lab to a Grid-based approach using multiple PC clusters with dynamically varying availability. Under support from an NSF CISE Research Resources grant, we have outfitted 52 Dell PCs in a student lab in our building that is operated by the University's Academic and Distributed Computing Services (ADCS) organization. This PC cluster supplements another PC cluster of 10 machines in our lab. As the students gradually leave this ADCS lab after 10 PM, the PCs are rebooted into an operating system image that sees the 400 GB disk subsystems we have installed on them and communicates with a central, 32-processor Unisys ES-7000 machine in our lab. The ES-7000 hosts databases that coordinate the work of these 52 PCs along with that of 10 additional Dell PCs in our lab that drive our PowerWall display. This equipment forms a local Grid that we coordinate to analyze and visualize data generated on remote clusters at NCSA. The PCs of the student lab offer a 20 TB pool of disk storage for our simulation data as well as a large movie rendering capability with their Nvidia graphics engines. However, these machines do not become available to us in force until after about 1 AM. This fact has forced us to automate our visualization process to an unusual degree. It has also forced us to address problems of security and run error diagnosis that we could easily avoid in a more standard environment. In this paper we report our methods of addressing these challenges and describe the software tools that we have developed and made available for this purpose on our Web site, www.lcse.umn.edu. We also report our experience in using this system to visualize 1.4 TB of vorticity volumetric data from a recent simulation of homogeneous, compressible turbulence with our PPM code. This code was run on the NSF TeraGrid cluster at NCSA, and the data was transported to our lab at 8 MB/sec over the Internet using the same ipRIO software that we developed to move this data around within our own environment. The move visualizations generated on the ADCS PCs overnight are viewed on the LCSE 10-panel, 13 Mpixel PowerWall the next day. Smaller trial movies can be generated on the small PC cluster in our lab before submitting an overnight large movie request.

In recent years, off-the-shelf graphics cards have provided the ability to program the graphics processing unit (GPU) as an alternative to using fixed function pipelines. We believe that this capability can enable a new paradigm in geoscience data visualization. In the past, the geoscience data preparation, interpretation, and simulation were all done by the central processing unit (CPU), and then the generated graphics primitives were fed into a GPU for visualization. This approach was dictated by the constraints imposed by the general-purpose graphics application programming interfaces (APIs). With GPU programming, this front-end processing can be done in the GPU and visualized immediately. After passing the geometry data into the GPU, parameters can be used to control these processes inside the GPU. The different algorithms associated with these processes can be applied at run time by loading a new shading program. To prove this concept, we designed and implemented Java-based shader classes, which operate on top of Cg, a high-level language for graphics programming. These shader classes load Cg shaders to provide a new method for visualizing and interacting with geoscience data. The results from this approach show better visual quality for seismic data display and dramatically improved performance for large 3D seismic data sets. For editing geological surfaces, tests demonstrate performance levels 10 times faster than the typical approach. This paper describes the use of these shaders and presents the results of shader application to geoscience data visualization.

The current rate of Mars exploration data acquisition demands that geoscientists and computer scientists coordinate central storage, processing and visualization strategies to anticipate future technological advancements. We investigate how existing 3-D visualization tools can be used to study a part of the Mars orbiter and lander data (about 4 terabytes of data). Our tools assist in juxtaposition of different datum and in viewing data that spans multiple orders of magnitude, specifically for current scientific research pertaining to Mars’ geophysics and geology. These tools also permit effective data fidelity and resolution assessment, allowing quick identification of problems related to the use of differing spatial coordinate systems, a continued problem. Knowledge gained from the small dataset we test, helps us identify key tools needed to accommodate the technology required to process and analyze approximately 64 terabytes of Mars data expected by 2008. We use the current planetary data archives, and identify key visualization techniques and tools that distill multiple data types into manageable end products. Our goal is to broaden the user base, using readily available platform-independent freeware packages, while simultaneously including sufficient modularity to be compatible with future technologies.

Information analysts must rapidly assess information to determine its usefulness in supporting and informing decision makers. In addition to assessing the content, the analyst must be confident about the quality and veracity of the information. Visualizations can concisely represent vast quantities of information, thus aiding the analyst to examine larger quantities of material; however, visualization programs are challenged to incorporate a notion of confidence or certainty because the factors that influence the certainty or uncertainty of information vary with the type of information and the type of decisions being made. For example, the assessment of potentially subjective human-reported data leads to a large set of uncertainty concerns in fields such as national security, law enforcement (witness reports), and even scientific analysis where data is collected from a variety of individual observers. What’s needed is a formal model or framework for describing uncertainty as it relates to information analysis, to provide a consistent basis for constructing visualizations of uncertainty. This paper proposes an expanded typology for uncertainty, drawing from past frameworks targeted at scientific computing. The typology provides general categories for analytic uncertainty, a framework for creating task-specific refinements to those categories, and examples drawn from the national security field.

Density differences are one of the main features users perceive in 2D scatter plots. However, because of pixels’ collisions, some areas become saturated and such differences are lost. To solve this problem, several proposals rely on sampling the dataset before visualizing it. Some of these introduce precise measures to understand the image degradation and use numerical differences in pixels to estimate density differences. It is our opinion that this issue deserves a deeper analysis, taking into account perceptual issues. In this paper we describe a study we conducted to understand the relationship between numerical pixel density and the perceived density. The results obtained were used to refine a sampling technique we developed to preserve relative densities in the context of 2D scatter plots.

Texture-based correspondence display is a methodology to display corresponding data elements in visual representations of complex multidimensional, multivariate data. A visual representation model is the abstract pattern used to transform numerical data to an image. It is challenging to develop visual representation models for multidimensional, multivariate data that are at once a comprehensive representation of the data and visually simple enough as to avoid confusion. Using multiple images increases the degrees of freedom of data representation with color or location serving as a means to show correspondence of the information among the images. The correspondence display techniques described utilize texture as a persistent medium to contain a visual representation model and as a means to create multiple renditions of data where color is used to identify correspondence. These techniques allow corresponding data elements to be displayed over a variety of visual metaphors in a normal rendering process without the addition of extraneous linking metadata creation and maintenance. Texture-based correspondence display extends the effectiveness of visual representation for understanding data to the understanding and creation of visual representation models.

The need for pattern discovery in long time series data led researchers to develop algorithms for similarity search. Most of the literature about time series focuses on algorithms that index time series and bring the data into the main storage, thus providing fast information retrieval on large time series. This paper reviews the state of the art in visualizing time series, and focuses on techniques that enable users to visually and interactively query time series. Then, it presents TimeSearcher 2, a tool that enables users to explore multidimensional data using synchronized tables and graphs with overview+detail, filter the time series data to reduce the scope of the search, select an existing pattern to find similar occurrences, and interactively adjust similarity parameters to narrow the result set. This tool is an extension of previous work, TimeSearcher 1, which uses graphical timeboxes to interactively query time series data.

We present an application case study for visualizing large data sets of time series spatial data. Our application is built on a flexible, object oriented framework that supports the visualization of dynamic internal wave propagation in the earth's tropopause. Our data model uses a multiresolution hierarchy that integrates spatial and temporal components. The data also includes error information at each level of the hierarchy. The application provides the scientist with tools necessary to examine, query, and interact with visualizations of data of interest.

The reorderable matrix method is a convenient way of representing static tabular data (i.e. matrices) visually. In this paper, we present an approach to use the reorderable matrix method for visualizing time-varying matrix data. Solutions to the problems encountered during the adaptation of this visualization method for time-varying matrices and proposals to solve the problems related to the automatic reordering of static tabular data are discussed. The approach is illustrated by visualizing sensitivity matrices generated during the simulation of metabolic network models.

This paper presents a qualitative and formative study of the uses of a starfield-based visualization interface for analysis of library collections. The evaluation process has produced feedback that suggests ways to significantly improve starfield interfaces and the interaction process to improve their learnability and usability. The study also gave us clear indication of additional potential uses of starfield visualizations that can be exploited by further functionality and interface development. We report on resulting implications for the design and use of starfield visualizations that will impact their graphical interface features, their use for managing data quality and their potential for various forms of visual data mining. Although the current implementation and analysis focuses on the collection of a physical library, the most important contributions of our work will be in digital libraries, in which volume, complexity and dynamism of collections are increasing dramatically and tools are needed for visualization and analysis.

One of the challenges of visualization software design is providing real-time tools capable of concurrently displaying data that varies temporally and in scale from kilometers to micrometers, such as the data prevalent in planetary exploration and deep-sea marine research. The Viz software developed by NASA Ames and the additions of the X-Core extensions solve this problem by providing a flexible framework for rapidly developing visualization software capable of accessing and displaying large dynamic data sets. This paper describes the Viz/X-Core design and illustrates the operation of both systems over a number of deployments ranging from marine research to Martian exploration. Highlights include a 2002 integration with live ship operations and the Mars Exploration Rovers Spirit and Opportunity.

Business Intelligence (BI) deals with transforming raw business data into valuable information for making decisions. The goal is to improve the operation and use of large-scale, complex information systems. A number of automated BI techniques are available. These methods, however, have to be supported by user interaction to make successful business decisions. In this paper, we present a new technique for interactive business intelligence based on visualization technology, called VisImpact. The basic idea of the VisImpact technique is to visually display the relationships between the important business operation parameters and the distribution of the process flow. We have applied VisImpact in the areas of business contract analysis, business operation analysis, and fraud analysis, to show the power of the VisImpact technique for finding process flows, patterns, and trends, and for a quick identification of exceptions (outliers). Our interactive VisImpact system provides the means for an instant drilldown to a transaction record level which allows observing the evolution of business dynamics.

The research on 3D model retrieval is a new hot point and meanwhile a difficult problem in the area of content based retrieval. In this paper, a novel 3D model retrieval and visualization engine, 3DMIRACLES (3D Multimedia Information RetrievAl, CLassification and Exploration System), has been developed, which integrates effective algorithms and techniques for both shape-based retrieval computation of 3D models and real-time visualization of the retrieval results in realistic 3D interactive mode. The system architecture of 3DMIRACLES has been proposed. For retrieval computation, new algorithms have been developed and implemented for 3D shape feature extraction and similarity matching, which mainly include slice based method, Delta functions method, thickness histogram method and directional form, etc. For interactive visualization, a novel 3D viewer has been developed based on the foundational programming library of 3D drawing developed by our group, which implements hybrid rendering method to make much simplification and shortcut processing of 3D rendering computation for achieving high speed and efficient real-time visualization of large scale 3D databases. Evaluation experiments show that 3DMIRACLES is successful and effective in 3D shape retrieval and visualization for large scale 3D model databases, and the research achievements may be applied to real application system.

Visual simulation can be efficiently performed using programmable graphics hardware. However, in utilizing hardware to maximize throughput, it is important not to constrain interactivity. We present a method of using the graphics hardware while maintaining full interactivity during simulation exploration. This interactivity involves: temporal exploration, data probing and modification, simulation model modification, and user defined visual metadata. Results are shown using our application for exploring a reaction-diffusion simulation.

Although there are a number of visualization systems to choose from when analyzing data, only a few of these allow for the integration of other visualization and analysis techniques. There are even fewer visualization toolkits and frameworks from which one can develop ones own visualization applications. Even within the research community, scientists either use what they can from the available tools or start from scratch to define a program in which they are able to develop new or modified visualization techniques and analysis algorithms. Presented here is a new general-purpose platform for constructing numerous visualization and analysis applications. The focus of this system is the design and experimentation of new techniques, and where the sharing of and integration with other tools becomes second nature. Moreover, this platform supports multiple large data sets, and the recording and visualizing of user sessions. Here we introduce the Universal Visualization Platform (UVP) as a modern data visualization and analysis system.

Scientific visualization is routinely promoted as an indispensable component of the knowledge discovery process in a variety of scientific and engineering disciplines. However, our experiences with visualization at the National Center for Atmospheric Research (NCAR) differ somewhat from those described by many in the visualization community. Visualization at NCAR is used with great success to convey highly complex results to a wide variety of audiences, but the technology only rarely plays an active role in the day-to-day scientific discovery process. We believe that one reason for this is the mismatch between the size of the primary simulation data sets produced and the capabilities of the software and visual computing facilities generally available for their analysis. Here we describe preliminary results of our efforts to facilitate visual as well as non-visual analysis of terascale scientific data sets with the aim of realizing greater scientific return from such large scale computation efforts.

Visualization of multidimensional data presents special challenges for the design of efficient out-of-core data access. Elements that are nearby in the visualization may not be nearby in the underlying data file, which can severely tax the operating system’s disk cache. The Granite Scientific Database System can address these problems because it is aware of the organization of the data on disk, and it knows the visualization method’s pattern of access. The access pattern is expressed using a toolkit of iterators that both describe the access pattern and perform the iteration itself. Because our system has knowledge of both the data organization and the access pattern, we are able to provide significant performance improvements while hiding the details of out-of-core access from the visualization programmer. This paper presents a brief description of our disk access system placing special emphasis on the benefits offered to a visualization application. We describe a simple demonstration application that shows dramatic performance improvements when used with the 39GB Visible Woman Dataset.

Secure management of file systems of large organizations can present significant challenges to system administrators, in terms of the number of users, shared access to parts of the file system for supporting large software projects, and securing and monitoring critical parts of the file system from intruders. We present interactive visualization tools for monitoring and viewing the complex access control relationships within large file systems. This tool is targeted as an aid to system administrators to manage users, software applications and shared access. We tested our tool on UNC Charlotte's Andrew File System (AFS), which contains 7043 users, 560 user groups, and about 2.1 million directories. Our system displays summary information about the file system, and two types of visualizations to explore access control relationships among classes of users. In addition, drill-down features are provided to explore the user file system structure and manage access control information of any directory within the system. All of the views are linked to permit easy navigation and features are provided that make the system scalable to larger filesystems.

Users of Information Retrieval systems have often been the target group of Human-Computer Interaction researchers. A lot of effort has been spent inventing new forms of visualizations to support the information seeking process. Information Retrieval and Information Visualization are tight coupled fields of research. Together with psychology (which answers questions like 'how' do users search) and usability engineering (answering questions like 'what' do user expect from user interfaces and their behavior) the research on improving information seeking systems goes on. This paper will concentrate on a meta-data driven, user-centered approach for the query formulation stage. In contrast to the intense research on result-set visualizations we will focus on the development of a visualization which supports human search behavior at the query stage. Additionally this visualization proved that it can compete with other visualizations like the scatter-plot as a visual filter in the result-set presentation.

The relative values of a single 2D scalar field can be effectively perceived when the field is represented by height and rendered as a surface in three dimensions. When multiple scalar fields are simultaneously rendered on the same domain, control of additional rendering attributes such as color, transparency, texturing, and lighting is needed for effective perception. However, the transition from foreground to background due to surface intersections can confound accurate surface perception, even for techniques that work well for overlapping non-intersecting surfaces. We propose a general method for decomposing and stratifying multiple intersecting surfaces into layered components to permit complete rendering control of each surface component. We explore different rendering schemes based on stratification values and report initial results from experimentation.

Virtual Reality (VR) is widely used in visualizing medical datasets. This interest has emerged due to the usefulness of its techniques and features. Such features include immersion, collaboration, and interactivity. In a medical visualization context, immersion is important, because it allows users to interact directly and closely with detailed structures in medical datasets. Collaboration on the other hand is beneficial, because it gives medical practitioners the chance to share their expertise and offer feedback and advice in a more effective and intuitive approach. Interactivity is crucial in medical visualization and simulation systems, because responsive and instantaneous actions are key attributes in applications, such as surgical simulations. In this paper we present a case study that investigates the use of VR in a collaborative networked CAVE environment from a medical volumetric visualization perspective. The study will present a networked CAVE application, which has been built to visualize and interact with volumetric datasets. We will summarize the advantages of such an application and the potential benefits of our system. We also will describe the aspects related to this application area and the relevant issues of such implementations.

Determining the neural connectivity of brain is an essential problem in neuroscience and the fluorescent imaging technique is a very useful to study this problem. In this technique, a real brain (typically of rat) is injected with a fluorescent dye and then sectioned into thin slices. Each slice is then exposed to illumination and a high-resolution image is captured. The areas in a slice that are impacted by the dye generate strong brightness due to fluorescence, and these regions reveal useful information on the neural connectivity. However, it is challenging to automatically register the image series. In this paper, we propose effective methods for the registration of fluorescent neural images. Our approach is based on the edge features of images. First, we use an effective method for edge detection. Then we adopt multi-level pattern recognition using clustering algorithms with the Mahalanobis distance criteria to isolate individual features. Finally, we adopt an elastic registration scheme using the thin-plate spline algorithm to solve the multivariate interpolation problem. Once all images are registered, we apply an elliptic weighted average (EWA) splatting technique for volume visualization. Our rendered results clearly display the 3D structures of the neural connectivity.

Visualization of the interface between settled solids and the optically opaque liquid above is necessary to facilitate efficient retrieval of the high-level radioactive waste (HLW) from underground storage tanks. A profiling sonar was used to generate 2-D slices across the settled solids at the bottom of the tank. By incrementally rotating the sonar about its centerline, slices of the solid-liquid interface can be imaged and a 3-D image of the settled solids interface generated. To demonstrate the efficacy of the sonar in real-time solid-liquid interface monitoring systems inside HLW tanks, two sets of experiments were performed. First, various solid objects and kaolin clay (10 μm dia) were successfully imaged while agitating with 30% solids (by weight) entrained in the liquid. Second, a solid with a density similar to that of the immersed fluid density was successfully imaged. Two dimensional (2-D) sonar images and the accuracy and limitations of the in-tank imaging will be presented for these two experiments. In addition, a brief review of how to utilize a 2-D sonar image to generate a 3-D surface of the settled layer within a tank will be discussed.

In an Adaptive Image Retrieval System (AIRS) the user-system interaction is built through an interface that allows the relevance feedback process to take place. Most existing image retrieval systems simply display the result list of images (or their thumbnails) to the user in a 2D grid, without including any information about the relationships between images. In this context, we propose a new interactive multiple views interface for our AIRS, in which each view illustrates these relations by using visual attributes (colors, shapes, proximities). We identify two types of users for an AIRS: a user who seeks images whom we refer to as an end-user, and a user who designs and researches the collection and the retrieval systems whom we refer to as a researcher-user. With such views, the interface allows user (end-user or researcher-user) more effective interaction with the system by seeing more information about the request sent to the system as well as, by better understanding of the results, how to refine the query iteratively. Our qualitative evaluation of these multiple views in AIRS shows that each view has its own limitations and benefits. However, together, the views offer complementary information that helps user in improving his or her search effectiveness.

A research paradigm is a dynamical system of scientific works, including their perceived values by peer scientists, and governed by intrinsic intellectual values and associated citation endurance and decay. Identifying an emerging research paradigm and monitoring changes in an existing paradigm have been a challenging task due to the scale and complexity involved. In this article, we describe an exploratory data analysis method for identifying a research paradigm based on clustering scientific articles by their citation half life and betweenness centrality as well as citation frequencies. The Expectation Maximization algorithm is used to cluster articles based on these attributes. It is hypothesized that the resultant clusters correspond to dynamic groupings of articles manifested by a research paradigm. The method is tested with three example datasets: Social Network Analysis (1992-2004), Mass Extinction (1981-2004), and Terrorism (1989-2004). All these subject domains have known emergent paradigms identified independently. The resultant clusters are interpreted and assessed with reference to clusters identified by co-citation links. The consistency and discrepancy between the EM clusters and the link-based co-citation clusters are also discussed.

Dynamic visualization provides a unique bridge between disciplines and offers a way to synthesize information from myriad sources into an integrated context. Geographic Information Systems “GIS” has evolved into a highly sophisticated toolset for "situated knowledge." Location-based services utilizing technologies such as GPS and RFID tagging are supporting the rise of spatially integrated information and are resulting in new visualization tools that provide a spatio-temporal context for multidisciplinary inquiry. This paper explores the following question: “How can geovisualization make the broadest range of data understandable in the most intuitive way?” It also introduces the GeoMatrix engine, a powerful tool for spherical realtime display. It assists in the assimilation of spatio-temporal information in a geographic context by showing patterns on multiple scales.

DNA microarray technology provides biologists with the ability to measure the expression level of thousands of genes in a single experiment. As data from such experiments accumulate, it will be essential to have accurate means for extracting biological significance and using the data to assign functions to genes. In this paper, we try to provide a clear view of DNA microarray gene expression data analysis and modelling process by combining novel and effective visualization techniques with data mining algorithms. As a result, an integrated framework has been proposed to model and visualize short, high-dimensional time series gene expression data. The framework reduces the dimensionality of variables before applying appropriate temporal modelling method. The framework takes gene expression data as input, clusters the genes, displays the clustering results using a novel graph layout algorithm, models individual gene clusters using Dynamic Bayesian Network and visualizes the modelling results using simple but effective visualization techniques. A prototype has been built using Java3D to visualize the framework. Various user interactions are added to make the system a more effective visualization tool. Database has also been linked with the system to provide biologists with more background information of the models.