Natural Language Interfaces that facilitate data exploration tasks are rapidly gaining in interest in the research community because they enable users to focus their attention on the task of inquiry rather than the mechanics of chart construction. Yet, current systems rely solely on processing the user’s explicit commands to generate the user’s intended chart. These commands can be ambiguous due to natural language tendencies such as speech disfluency and underspecification. In this paper, we developed and studied how an always listening interface can help contextualize imprecise queries. Our study revealed that an always listening interface is able to use an on-going conversation to fill in missing properties for imprecise commands, disambiguate inaccurate commands without asking the user for clarification, as well as generate charts without being explicitly asked.

Abari Bhattacharya, Abhinav Kumar, Barbara Di Eugenio, Roderick Tabalba, Jillian Aurisano, Veronica Grosso, Andrew Johnson, Jason Leigh, Moira Zellner. Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. 2023.

Abstract This article discusses the design and implementation of the Hawai'i Groundwater Recharge Tool, an application for providing data and analyses of the impacts of land‐cover modifications and changes in precipitation on groundwater‐recharge rates for the island of O'ahu. This application uses simulation data based on a set of 29 land‐cover types and 2 precipitation conditions to provide users with real‐time recharge calculations for interactively defined land‐cover modifications. The tool provides two visualizations, representing the land cover for the island and the resultant groundwater‐recharge rates, and a set of metrics indicating the changes to groundwater recharge for relevant areas to present a set of easily interpretable outcomes based on user‐defined scenarios. Users have varying degrees of control over the granularity of data input and output, allowing for the quick production of a roughly defined scenario, or more precise land‐cover definitions. These modifications can be exported for further analysis. Heuristics are used to provide a responsive user interface and performant integration with the database containing the full set of simulation data. This tool is designed to provide user‐friendly access to the information on the impacts of land‐cover and precipitation changes on groundwater‐recharge rates needed to assist in making data‐driven decisions.

Representing branching and comparative analyses in computational notebooks is complicated by the 1-dimensional (1D), top-down list arrangement of cells. Given the ubiquity of these and other non-linear features, their importance to analysis and narrative, and the struggles current 1D computational notebooks have, enabling organization of computational notebook cells in 2 dimensions (2D) may prove valuable. We investigated whether and how users would organize cells in such a "2D Computational Notebook" through a user study and gathered feedback from participants through a follow-up survey and optional interviews. Through the user study, we found 3 main design patterns for arranging notebook cells in 2D: Linear, Multi-Column, and Workboard. Through the survey and interviews, we found that users see potential value in 2D Computational Notebooks for branching and comparative analyses, but the expansion from 1D to 2D may necessitate additional navigational and organizational aids.

Technology have long been a partner of workplace meeting facilitation. The recent outbreak of COVID-19 and the cautionary measures to reduce its spread have made it more prevalent than ever before in the form of online-meetings. In this paper, we recount our experiences during weekly meetings in three modalities: using SAGE2 - a collaborative sharing software designed for large displays - for co-located meetings, using a conventional projector for co-located meetings, and using the Zoom video-conferencing tool for distributed meetings. We view these meetings through the lens of effective meeting attributes and share ethnographic observations and attitudinal survey conducted in our research lab. We discuss patterns of content sharing, either sequential, parallel, or semi-parallel, and the potential advantages of creating complex canvases of content. We see how the SAGE2 tool affords parallel content sharing to create complex canvases, which represent queues of ideas and contributions (past, present, and future) using the space on a large display to suggest the progression of time through the meeting.

SAGE (the Scalable Adaptive Graphics Environment) and its successor SAGE2 (the Scalable Amplified Group Environment) are operating systems for managing content across wideband display environments. This paper documents the prevalent usage patterns of SAGE-enabled display walls in support of the e-Science enterprise, based on nearly 15 years of observations of the SAGE community. These patterns will help guide e-Science users and cyberinfrastructure developers on how best to leverage large tiled display walls, and the types of software services that could be provided in the future.

The Scalable Amplified Group Environment (SAGE2) is an open-source, web-based middleware for driving tiled display walls. SAGE2 allows running multiple applications at once within its workspace. In large display walls, users tend to collaborate using multiple applications in the same space for simultaneous interaction and review. Unfortunately, many of these applications were created independently by different developers and were never intended to interoperate which greatly limits their potential reusability. SAGE2 developers face system limitations where applications are data segregated and cannot easily communicate with others. To counter this problem, we developed the SAGE2 data sharing components. We describe the Sage River Disaster Information (SageRDI) application and the SAGE2 architectural implementations necessary for its operation. SageRDI enables river.go.jp, an existing website that provides water sensor data for Japan, to interact with other SAGE2 applications without modifying the website's server, hosted files, nor any of the default SAGE2 applications.

Mixed presence collaboration involves remote collaboration between multiple collocated groups. This paper presents the design and results of a user study that focused on mixed presence collaboration using large-scale tiled display walls. The research was conducted in order to compare data synchronization schemes for multi-user visualization applications. Our study compared three techniques for sharing data between display spaces with varying constraints and affordances. The results provide empirical evidence that using data sharing techniques with continuous synchronization between the sites lead to improved collaboration for a search and analysis task between remotely located groups. We have also identified aspects of synchronized sessions that result in increased remote collaborator awareness and parallel task coordination. It is believed that this research will lead to better utilization of large-scale tiled display walls for distributed group work.

Abhinav Kumar, Jillian Aurisano, Barbara Di Eugenio, Andrew Johnson, Alberto Gonzalez, Jason Leigh. Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2016.

Real world group-to-group collaboration often occurs between partially distributed interdisciplinary teams, with each discipline working in a unique environment suited for its needs. Groupware must be flexible so that it can be incorporated into a variety of workspaces in order to successfully facilitate this type of mixed presence collaboration. We have developed two new techniques for sharing and synchronizing multi-user applications between heterogeneous large-scale shared displays. The first new technique partitions displays into a perfectly mirrored public space and a local private space. The second new technique enables user-controlled partial synchronization, where different attributes of an application can be synchronized or controlled independently. This paper presents two main contributions of our work: 1) identifying deficiencies in current groupware for interacting with data during mixed presence collaboration, and 2) developing two multi-user data synchronization techniques to address these deficiencies and extend current collaborative infrastructure for large-scale shared displays.

Large high-resolution displays are becoming increasingly common in research settings, providing data scientists with visual interfaces for the analysis of large datasets. Numerous studies have demonstrated unique perceptual and cognitive benefits afforded by these displays in visual analytics and information visualization tasks. However, the effects of these displays on knowledge discovery in exploratory visual analysis are still poorly understood. We present the results of a small-scale study to better understand how display size and resolution affect insight. Analyzing participants' verbal statements, we find preliminary evidence that larger displays with more pixels can significantly increase the number of discoveries reported during visual exploration, while yielding broader, more integrative insights. Furthermore, we find important differences in how participants performed the same visual exploration task using displays of varying sizes. We tie these results to extant work and propose explanations by considering the cognitive and interaction costs associated with visual exploration.

Achievable networking performance of applications in a supercomputer depends on the exact combination of the communication patterns of the applications and the routing algorithms used by the supercomputer. In order to achieve the highest networking performance for the applications the routing algorithms need to be designed optimally for those communication patterns. However, while communication patterns usually have a wide variation from application to application and even from phase to phase in an application, routing algorithms have a limited variation and usually are optimized for typical communication patterns. This results in high networking performance for favored communication patterns but low networking performance for others. In this paper we present approaches for improving networking performance by rebalancing load on physical links on the Blue Gene Q supercomputer. We realize our approaches in a framework called OPTIQ and demonstrate the efficacy of our framework via a set of benchmarks. Our results show that we can achieve 30% higher throughput on experiment with data and patterns from a real application. The improvement can be up to several times higher throughput than default MPI_Alltoallv used in the Blue Gene Q supercomputer for certain communication patterns.

Achievable networking performance of applications in a supercomputer depends on the exact combination of the communication patterns of the applications and the routing algorithms used by the supercomputer. In order to achieve the highest networking performance for the applications, the routing algorithms need to be designed optimally for those communication patterns. However, while communication patterns usually vary from application to application and even from phase to phase in an application, routing algorithms have limited variation and usually are optimized for typical communication patterns. This results in high networking performance for some communication patterns. In this paper we present approaches for improving communication performance by using multiple paths and re-balancing load on physical links on the Blue Gene/Q supercomputer. We realize our approaches in a framework called OPTIQ and demonstrate the efficacy of our framework via a set of benchmarks. Our results show that we can achieve 43 -- 67% higher throughput on average from 91 experiments, and can achieve higher throughput than default MPI_Alltoallv used for certain communication patterns.

BACKGROUND: The volume of complete bacterial genome sequence data available to comparative genomics researchers is rapidly increasing. However, visualizations in comparative genomics--which aim to enable analysis tasks across collections of genomes--suffer from visual scalability issues. While large, multi-tiled and high-resolution displays have the potential to address scalability issues, new approaches are needed to take advantage of such environments, in order to enable the effective visual analysis of large genomics datasets. RESULTS: In this paper, we present Bacterial Gene Neighborhood Investigation Environment, or BactoGeNIE, a novel and visually scalable design for comparative gene neighborhood analysis on large display environments. We evaluate BactoGeNIE through a case study on close to 700 draft Escherichia coli genomes, and present lessons learned from our design process. CONCLUSIONS: BactoGeNIE accommodates comparative tasks over substantially larger collections of neighborhoods than existing tools and explicitly addresses visual scalability. Given current trends in data generation, scalable designs of this type may inform visualization design for large-scale comparative research problems in genomics.

Current web-based collaboration systems, such as Google Hangouts, WebEx, and Skype, primarily enable single users to work with remote collaborators through video conferencing and desktop mirroring. The original SAGE software, developed in 2004 and adopted at over one hundred international sites, was

In this paper, we propose an approach to improving the I/O performance of an IBM Blue Gene/Q supercomputing system using a novel framework that can be integrated into high performance applications. We take advantage of the system's tremendous computing resources and high interconnection bandwidth among compute nodes to efficiently exploit I/O bandwidth. This approach focuses on lossless data compression, topology-aware data movement, and subfiling. The efficacy of this solution is demonstrated using microbenchmarks and an application-level benchmark.

In the domain of large-scale visualization instruments, hybrid reality environments (HREs) are a recent innovation that combines the best-in-class capabilities of immersive environments, with the best-in-class capabilities of ultra-high-resolution display walls. HREs create a seamless 2D/3D environment that supports both information-rich analysis as well as virtual reality simulation exploration at a resolution matching human visual acuity. Co-located research groups in HREs tend to work on a variety of tasks during a research session (sometimes in parallel), and these tasks require 2D data views, 3D views, linking between them and the ability to bring in (or hide) data quickly as needed. In this paper we present Omegalib, a software framework that facilitates application development on HREs. Omegalib is designed to support dynamic reconfigurability of the display environment, so that areas of the display can be interactively allocated to 2D or 3D workspaces as needed. Compared to existing frameworks and toolkits, Omegalib makes it possible to have multiple immersive applications running on a cluster-controlled display system, have different input sources dynamically routed to applications, and have rendering results optionally redirected to a distributed compositing manager. Omegalib supports pluggable front-ends, to simplify the integration of third-party libraries like OpenGL, OpenSceneGraph, and the Visualization Toolkit (VTK). We present examples of applications developed with Omegalib for the 74-megapixel, 72-tile CAVE2™ system, and show how a Hybrid Reality Environment proved effective in supporting work for a co-located research group in the environmental sciences.

This video describes the CAVE2 Hybrid Reality Environment developed by the Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago (UIC). CAVE2 is a near-seamless flat-panel-based, surround-screen immersive system that can simultaneously display both 2D and 3D information, providing more flexibility for mixed media applications and more opportunities for groups of researchers to work together with large heterogeneous datasets [1]. CAVE2 is a cylindrical system of 24 feet in diameter and 8 feet tall, and consists of 72 near-seamless, off-axis-optimized passive-stereo LCD panels, creating a 320-degree panoramic environment for displaying information at 37 Megapixels (in stereoscopic 3D) or 74 Megapixels in 2D and at a horizontal visual acuity of 20/20 [2]. Custom LCD panels with shifted polarizers were built so the images in the top and bottom rows of LCDs are optimized for vertical off-center viewing, allowing viewers to come closer to the displays while minimizing ghosting. EVL's Omegalib middleware supports fully immersive OpenGL, OpenSceneGraph and VTK applications, as well as EVL's SAGE middleware to achieve a hybrid 2D/3D environment. CAVE2 also supports CalVR (developed at the Calit2 Qualcomm Institute at University of California, San Diego), Electro (by Robert Kooima, currently at Louisiana State University) and Google Earth.

This article will describe the progress since 2000 on research and development in 2-D and 3-D scalable resolution display walls that are built from tiling individual lower resolution flat panel displays. The article will describe approaches and trends in display hardware construction, middleware architecture, and user-interaction design. The article will also highlight examples of use cases and the benefits the technology has brought to their respective disciplines.

Abstract Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph‐based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time. In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.

Ultra-high-resolution tiled-display walls are typically driven by a cluster of computers. Each computer may drive one or more displays. Synchronization between the computers is necessary to ensure that animated imagery displayed on the wall appears seamless. Most tiled-display middleware systems are designed around the assumption that only a single application instance is running in the tiled display at a time. Therefore synchronization can be achieved with a simple solution such as a networked barrier. When a tiled display has to support multiple applications at the same time, however, the simple networked barrier approach does not scale. In this paper we propose and experimentally validate two synchronization algorithms to achieve low-latency, intertile synchronization for multiple applications with independently varying frame rates. The two-phase algorithm is more generally applicable to various highresolution tiled display systems. The one-phase algorithm provides superior results but requires support for the Network Time Protocol and is more CPU-intensive.

Several studies have analyzed the link between mental dysfunctions and eye movements, using eye tracking techniques to determine where a person is looking, that is, the fixations. In this paper, we present a novel methodology to improve current diagnosis and evaluation methods of attention disorders. We have developed and tested several data-mining methodologies suitable for the automatic analysis and visualization of eye tracking data. In particular three novel methods of classification of subjects are proposed: (i) a method that uses expectation maximization to classify according to statistical likelihood of fixations locations; (ii) a procedure based on the Levenshtein distance method to compare sequences of fixations; and (iii) a method based on the analysis of the transitions frequencies of fixations between regions. Results of evaluation of classification accuracy are finally presented.

Many interrelated planetary height map and surface image map data sets exist, and more data are collected each day. Broad communities of scientists require tools to compose these data interactively and explore them via real-time visualization. While related, these data sets are often unregistered with one another, having different projection, resolution, format, and type. We present a GPU-centric approach to the real-time composition and display of unregistered-but-related planetary-scale data. This approach employs a GPGPU process to tessellate spherical height fields. It uses a render-to-vertex-buffer technique to operate upon polygonal surface meshes in image space, allowing geometry processes to be expressed in terms of image processing. With height and surface map data processing unified in this fashion, a number of powerful composition operations may be uniformly applied to both. Examples include adaptation to nonuniform sampling due to projection, seamless blending of data of disparate resolution or transformation regardless of boundary, and the smooth interpolation of levels of detail in both geometry and imagery. Issues of scalability and precision are addressed, giving out-of-core access to giga-pixel data sources, and correct rendering at scales approaching one meter.

We present a novel rails approach so that future e-Science applications can effectively exploit future system architectures, including multi-core and many-core architectures, multiple network cards, multiple graphical processing units and hybrid hierarchical memory architectures. We define "rail" as the co-scheduling of two or more of these resources. This approach enables creation of parallel multi-rails through every aspect of an end system: from processing on the multi- and many cores, to generation of multiple data flows and streaming over multi-lane network interface card (NIC) connected via a parallel interconnect. We describe a novel open-source multi-rail toolkit and the evaluation of end-system parameters that impact the efficiency of such multi-rail systems, including Interrupt, Memory, Thread and Core Affinities -- key properties for achieving scalable performance.

LambdaRAM is a high-performance, multidimensional, wide-area, distributed cache that takes advantage of massively available memory from multiple clusters interconnected by ultra high-speed networking to provide data-intensive scientific applications with rapid access to both local and remote data without suffering the latency bottlenecks often associated with large storage systems and wide-area data access. LambdaRAM has been demonstrated to yield significant performance speed-ups for geophysical and Bioscience applications accessing extremely large datasets. Currently, LambdaRAM is being integrated by NASA for the modelling, analysis and prediction (MAP) program applications to study tropical cyclones. Formal verification o/LambdaRAM is important to NASA to ensure that LambdaRAM operates reliably in real-time mission critical deployments. We present our preliminary steps towards full formal verification of LambdaRAM. We first give an abstract description of the system and then verify several of its properties. Most of the proofs are accomplished by automatic techniques, while some require deductive steps.

A solid-state dynamic parallax barrier autostereoscopic display mitigates some of the restrictions present in static barrier systems, such as fixed view-distance range, slow response to head movements, and fixed stereo operating mode. By dynamically varying barrier parameters in real time, viewers may move closer to the display and move faster laterally than with a static barrier system, and the display can switch between 3D and 2D modes by disabling the barrier on a per-pixel basis. Moreover, Dynallax can output four independent eye channels when two viewers are present, and both head-tracked viewers receive an independent pair of left-eye and right-eye perspective views based on their position in 3D space. The display device is constructed by using a dual-stacked LCD monitor where a dynamic barrier is rendered on the front display and a modulated virtual environment composed of two or four channels is rendered on the rear display. Dynallax was recently demonstrated in a small-scale head-tracked prototype system. This paper summarizes the concepts presented earlier, extends the discussion of various topics, and presents recent improvements to the system.

A novel barrier strip autostereoscopic (AS) display is demonstrated using a solid-state dynamic parallax barrier. A dynamic barrier mitigates restrictions inherent in static barrier systems such as fixed view distance range, slow response to head movements, and fixed stereo operating mode. By dynamically varying barrier parameters in real time, viewers may move closer to the display and move faster laterally than with a static barrier system. Furthermore, users can switch between 3D and 2D modes by disabling the barrier. Dynallax is head-tracked, directing view channels to positions in space reported by a tracking system in real time. Such head-tracked parallax barrier systems have traditionally supported only a single viewer, but by varying the barrier period to eliminate conflicts between viewers, Dynallax presents four independent eye channels when two viewers are present. Each viewer receives an independent pair of left and right eye perspective views based on their position in 3D space. The display device is constructed using a dual-stacked LCD monitor where a dynamic barrier is rendered on the front display and the rear display produces a modulated VR scene composed of two or four channels. A small-scale head-tracked prototype VR system is demonstrated.

Advance reservation is a mechanism to guarantee the availability of resources when they are needed. In the context of LambdaGrid, this mechanism is used to provide data-intensive applications with the needed deterministic network quality of service to transport data between grid instruments, high-performance storage systems, compute clusters and visualization systems. Flexible scheduling affords users greater convenience while also improving resource utilization and acceptance rate. In this paper we propose a flexible advance reservation model (FARM) and describe how to implement this model in the meta-scheduling problem. Then we extend this methodology to the cross-domain lightpath reservation problem by incorporating routing and wavelength assignment algorithms. Next, we present the architecture, implementation and APIs of a coordinated interdomain and intradomain optical control plane called AR-PIN/PDC, which is capable of flexible advance reservations. Our simulation results show that by relaxing the reservation time constraint, the acceptance rate and resource utilization can be improved dramatically. Through simulations, we also analyze the impact of advance reservations on immediate reservations and conclude that both AR and IR requests need admission control algorithms in order to let both types of reservations coexist and use resource properly.

LambdaGrid applications as typified by data-intensive collaborative visualization are likely to be the first users of terabit-level networking. This paper features a main enabler of collaborative visualization over LambdaGrid, the Scalable Adaptive Graphics Environment (SAGE) in particular, and anticipates the future needs of LambdaGrid applications in general. We present a scalable architecture called LambdaBridge to 'bridge' LambdaGrid applications with the terabit optical core networks. LambdaBridge will provision and control predictable- performance networks for end-systems (i.e. Grid clusters) using on-demand lambda/VLAN provisioning and end-system traffic shaping. The chief contribution of LambdaBridge is a deep understanding of how to synergistically bridge provisionable networks, end-systems, and future generation distributed terabit applications. This paper also introduces Scalable Visualcasting - a multicasting service for LambdaGrid that incorporates IP multicasting and optical multicasting.

A collection of Grid computing resources interconnected by an application-configurable network of lightpaths is called a LambdaGrid. It provides data-intensive applications with the needed deterministic network bandwidth to transport data between grid instruments, high-performance storage systems, compute clusters and visualization systems, that is often needed for real-time interactive scientific exploration. Advance reservation is needed to guarantee the availability of network resources. Flexible scheduling affords users greater convenience while also improving resource utilization and acceptance rate. In this paper we propose a unified flexible advance reservation model called FARM and apply this model to the cross-domain Routing and Wavelength Assignment problem. We will present the architecture and implementation of a coordinated Interdomain and Intradomain optical control plane, which is capable of flexible advance reservations. Our simulation results show that by just relaxing the reservation constraint and providing flexibility on the starting time, the network can carry 39% more load, and resource utilization can improve by 41%.

The Scalable Adaptive Graphics Environment (SAGE) is specialized middleware for enabling data, high-definition video and extremely high-resolution graphics to be streamed in real-time from remotely distributed rendering and storage clusters to scalable display walls over ultra-high-speed networks. In this paper, we present the SAGE architecture, focusing on its dynamic graphics streaming capability. In the SAGE framework, multiple visualization applications can be streamed to large tiled displays and viewed at the same time. The application windows can be moved, resized and overlapped like any standard desktop window manager. Every window movement or resize operation requires dynamic and non-trivial reconfiguration of the involved graphics streams. This approach has been successfully shown to scale to support streaming on the LambdaVision 100 Megapixel display wall. SAGE is now being extended to support distance collaboration with multiple endpoints by streaming visualization to all the participants.

We developed a testbed containing: real time data from over 830 highway traffic sensors in the Chicago region, data about weather, and text data about events that might affect traffic. The goal was to detect in real time interesting changes in traffic conditions. Given the size and complexity of the data, we choose to build a large number of separate baseline models. We built a separate baseline for each hour in the day, for each day in the week, and for every 2 or 3 traffic sensors, resulting in over 42,000 separate baseline models. We also built a baseline engine to build the necessary baselines automatically. We modified an open source scoring engine to process in real time each new sensor reading, update the appropriate feature vectors, score the updated feature vectors using the baseline models, and send out real time alerts when deviations from the baselines were detected.

JuxtaView is a cluster-based application for viewing ultra-high-resolution images on scalable tiled displays. We present in JuxtaView, a new parallel computing and distributed memory approach for out-of-core montage visualization, using LambdaRAM, a software-based network-level cache system. The ultimate goal of JuxtaView is to enable a user to interactively roam through potentially terabytes of distributed, spatially referenced image data such as those from electron microscopes, satellites and aerial photographs. In working towards this goal, we describe our first prototype implemented over a local area network, where the image is distributed using LambdaRAM, on the memory of all nodes of a PC cluster driving a tiled display wall. Aggressive prefetching schemes employed by LambdaRAM help to reduce latency involved in remote memory access. We compare LambdaRAM with a more traditional memory-mapped file approach for out-of-core visualization.

In electronically mediated distance collaborations involving scientific data, there is often the need to stream the graphical output of individual computers or entire visualization clusters to remote displays. This work presents TeraVision as a scalable platform-independent solution which is capable of transmitting multiple synchronized high-resolution video streams between single workstations and/or clusters without requiring any modifications to be made to the source or destination machines. Issues addressed include: how to synchronize individual video streams to form a single larger stream; how to scale and route streams generated by an array of M/spl times/N nodes to fit a X/spl times/Y display; and how TeraVision exploits a variety of transport protocols. Results from experiments conducted over gigabit local-area networks and wide-area networks (between Chicago and Amsterdam), are presented. Finally, we propose the scalable adaptive graphics environment (SAGE) - an architecture to support future collaborative visualization environments with potentially billions of pixels.

We present the current state of Vol-a-Tile, an interactive tool for exploring large volumetric data on scalable tiled displays. Vol-a-Tile presents a variety of features employed by scientists at the Scripps Institution of Oceanography on data collected from the Anatomy of a Ridge-Axis Discontinuity seismic experiment. Hardware texture mapping and level-of-detail techniques provide interactivity. A high-performance network protocol is used to connect remote data sources over high-bandwidth photonic networks.

High speed bulk data transfer is an important part of many data-intensive scientific applications. This paper describes an aggressive bulk data transfer scheme, called Reliable Blast UDP (RBUDP), intended for extremely high bandwidth, dedicated- or Quality-of-Service-enabled networks, such as optically switched networks. This paper also provides an analytical model to predict RBUDP's performance and compares the results of our model against our implementation of RBUDP. Our results show that RBUDP performs extremely efficiently over high speed dedicated networks and our model is able to provide good estimates of its performance.

This paper presents our findings after the first year and a half of a multi-year deployment of an ImmersaDesk/sup (R)/ to a local elementary school, investigating its effectiveness in enhancing science education. These findings deal with how VR can aid in the coordination of multiple representations, and how to integrate the technology into the existing school culture.

This paper describes the design and implementation of CAVERNsoft G2, a toolkit for building collaborative virtual reality applications. G2's special emphasis is on providing the tools to support high-performance computing and data intensive systems that are coupled to collaborative, immersive environments.This paper describes G2's broad range of services, and demonstrates how they are currently being used in a collaborative volume visualization application.

This paper proposes a methodology for employing collaborative, immersive virtual environments as a high-end visualization interface for massive data-sets. The methodology employs feature detection, partitioning, summarization and decimation to significantly cull massive data-sets. These reduced data-sets are then distributed to the remote CAVEs, ImmersaDesks and desktop workstations for viewing. The paper also discusses novel techniques for collaborative visualization and meta-data creation.

The paper discusses a virtual mail system. V-mail is designed to be used in the CAVE virtual environment. The participants' mail messages and the ongoing modifications of the VE are maintained by a central server. V-mail's user interface is embodied in a virtual friend or pet that follows the participant as he/she interacts with the VE.

This paper presents an overview of the tele-immersion applications that have been built by collaborators around the world using the CAVERNsoft toolkit, and the lessons learned from building these applications. In particular the lessons learned are presented as a set of rules-of-thumb for developing tele-immersive applications in general.

The development of applications and tools for high-performance "computational grids" is complicated by the heterogeneity and frequently dynamic behavior of the underlying resources; by the complexity of the applications themselves, which often combine aspects of supercomputing and distributed computing; and by the need to achieve high levels of performance. The Globus toolkit has been developed with the goal of simplifying this application development task, by providing implementations of various core services deemed essential for high-performance distributed computing. In this paper, we describe two large applications developed with this toolkit: a distributed interactive simulation and a teleimmersion system. We describe the process used to develop the applications, review the lessons learned and draw conclusions regarding the effectiveness of the toolkit approach.

The Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago (UIC) has partnered with dozens of computational scientists and engineers to create visualization and virtual reality (VR) devices and applications for collaborative exploration of scientific and engineering data. Since 1995, our research and development activities have incorporated emerging high bandwidth networks like the vBNS and the Internet2 in an effort now called Tele-Immersion. As a result of our six years' experience in building first and second generation VR devices to support these applications, we consider third generation VR devices that will provide desktop/office sized displays. Since no current technology is yet configurable with ideal resolution and size, we will first simulate these devices with available parts, and then build more advanced prototypes. We believe that the devices we propose to build using the new display technologies form a set of desirable human/computer interface requirements for successful Tele-Immersion adoption. A goal of this research is to develop clearly compelling prototypes so that these devices can be improved and reproduced by the private sector.

CAVERN, the CAVE Research Network, is an alliance of industrial and research institutions equipped with CAVE-based virtual reality hardware and high-performance computing resources, interconnected by high-speed networks, to support collaboration in design, education, engineering, and scientific visualization.CAVERNsoft is the collaborative software backbone for CAVERN. CAVERNsoft uses distributed data stores to manage the wide range of data volumes (from a few bytes to several terabytes) that are typically needed for sustaining collaborative virtual environments. Multiple networking interfaces support customizable, latency, data consistency, and scalability that are needed to support a broad spectrum of networking requirements. These diverse database and networking requirements are characteristics typically unexhibited by previous desktop multimedia systems but are common in real-time immersive virtual reality applications.