Visualization management systems which integrate database management, data visualization, and graphical user-interface generation into one package are becoming essential tools for conducting science. Unfortunately, the link between data management and data visualization is not very well understood. To make matters worse, most database management systems available today are not well- suited for handling large, time-sequenced data sets that are common to many scientific disciplines. The reason for this shortfall is that most database systems do not use an appropriate data model, are not geared toward real-time operation, and they have inadequate user-interfaces. Hints as to how database systems can fix these problems are given so that effective visualization management systems can be constructed.

The systems integration questions really extend beyond database and visualization aspects to include external data processing services such as numerical compute engines, statistical packages, neural networks, and the like. In constructing a system, several different design dimensions must be considered, such as ease of use, performance, size of data, coupling of systems, extensibility, heterogeneity, migration paths, distribution, etc., as outlined previously. In addition, in designing systems such as dataflow or object oriented modeled systems, it may be possible to connect two different procedures, or operations over data, but it may not be meaningful to do so. Efforts aimed at improving the integration of visualization and database systems are currently underway (e.g. Sequoia 2000, Aurora from Xidak, and PAGEIN), differing in scope and design goals. We hope that heightened awareness of the needs of users with data management and visualization problems will further increase interaction between developers of database and visualization systems in the near future, to address the numerous challenges that lie ahead.

A new prototype, interactive visualization system is described which is designed to allow anyone to browse for and then visualize data within general data spaces. The prototype, called Tecate, capitalizes on the architectural strengths of current scientific visualization systems, network browsers like Mosaic, database management system front-ends, and on virtual reality systems. Tecate is able to browse for data contained in databases managed by database management systems, and it can browse for information contained in the World Wide Web. In addition, Tecate dynamically crafts user-interfaces and interactive visualizations of selected data-sets with the aid of an intelligent system. This system automatically maps most kinds of data into a virtual world that can be explored directly by end-users.

Tecate is a new infrastructure on which applications can be constructed that allow end users to browse for and then visualize data within networked data sources. This software platform capitalizes on the architectural strengths of current scientific visualization systems, network browsers like Netscape, database management system front ends, and virtual reality systems. Applications layered on top of Tecate are able to browse for information in databases managed by database management systems and for information contained in the World Wide Web. In addition, Tecate dynamically crafts user interfaces and interactive visualizations of selected data sets with the aid of an intelligent system. This system automatically maps many kinds of data sets into a virtual world that can be explored directly by end users. In describing these virtual worlds, Tecate uses an interpretive language that is also capable of performing arbitrary computations and mediating communications among different processes.

Addresses the needs and requirements of integrating visualization and geographic information system technologies. There are three levels of integration methods: rudimentary, operational and functional. The rudimentary approach uses the minimum amount of data sharing and exchange between these two technologies. The operational level attempts to provide consistency of the data while removing redundancies between the two technologies. The functional form attempts to provide transparent communication between these respective software environments. At this level, the user only needs to request information and the integrated system retrieves or generates the information depending upon the request. This paper examines the role and impact of these three levels of integration. Stepping further into the future, the paper also questions the long-term survival of these separate disciplines

The ability directly to access, analyze, share and visualize Earth science data has become extremely important for monitoring global environment change. The unique characteristics of Earth science information places undue burdens not only on computing systems but also on the scientists themselves, who must make many low-level browsing and visualization decisions in the course of their work. To address this problem, an effort has been taken to develop a visualization management system (VMS) that insulates the data analyst from making these low-level decisions. The VMS is based on an integrated architecture that utilizes both a database and a knowledgebase management system to generate visualizations of Earth science data. The heart of this system is an intelligent visualization subsystem (IVS) that makes use of a knowledge representation system to help construct interactive data visualizations. With the system, end users specify what to visualize while the system determines how the visualization is to be performed. Therefore, end users will be able to concentrate more on analyzing data rather than on the processes of gathering and visualizing it.

Non-visualization experts, including most scientists, find visualization systems like AVS too difficult to use. One approach to assisting these end-users in doing interactive visualization is to embed the knowledge of visualization experts into an intelligent system. A prototype, called Tecate, of such a system has been devel- oped as part of the Sequoia 2000 Project. In this system, a Planner makes use of expert knowledge stored in a Knowledge Base to create data-flow visualization programs. The Planner takes as input a description of the data to be visualized and an indication of the data analysis goals of an end-user. From this information, an AVS network script is produced that when executed, builds an appropriate visualization of the indicated data set. The networks so produced make use of both a restricted set of standard AVS modules and a collection of custom ones which operate on data structured as fiber bundles.

A prototype visualization management system is described which merges the capabilities of a database management system with any number of existing visualization packages such as AVS or IDL. The prototype uses the Postgres database management system to store and access Earth science data through a simple graphical browser. Data located in the database is visualized by automatically invoking a desired visualization package and downloading an appropriate script or program. The central idea underlying the system is that information on how to visualize a dataset is stored in the database with the dataset itself. As a result, scientists can concentrate more on their science rather than on the process of doing it since visualization programs do not have to be created or searched for each time a dataset is to be viewed.

An architecture is described for the Tioga Visualization Management System which is under development as part of the Sequoia 2000 Project. This system brings together the capabilities of a database management system a scientific visualization system, and a graphical user-interface builder. The paper concentrates on the front-end of Tioga which is interactive visualizations of data that reside in a database management system. The Visualization Executive achieves this goal by mixing techniques from knowledge-based systems with those of scientific visualization and user-interface design. The intent is to free scientists as much as possible from having to deal with the process of doing science so that they can concentrate on the science itself.

A new computer graphics algorithm which simulates the propagation of light and its interaction with matter on a massively parallel machine is presented. This algorithm, called the Tagged Shooting Method, is designed for a virtual computer containing a great number of simple, communicating processors arrayed into a cubical, three-dimensional lattice. Only nearest-neighbor communication among processors is assumed and there is no reliance on global shared memory. The algorithm is similar in spirit to the classical Progressive Refinement Radiosity Method designed for more conventional computers but is not an adaptation of that technique to massive parallelism. Instead, the new algorithm uses a discretization of the wave equation as a local rule for shuttling radiant energy values between processors which correspond to regions of space. A number of example images that were created with an implementation of the algorithm on a Connection Machine are depicted and critiqued.

An architecture is described for the Tioga Visualization Management Systemwhich is under development as part of the Sequoia 2000 Project. This system brings together the capabilities of a database management system, a scientific visualization system, and a graphical user-interface buil der. The paper concentrates on the front-end of Tioga which is called the Visualization Executive. The Visualization Executive is designed to allow scientists to easily do their own interactive visualizations of data that reside in a databas e management system. The Visualization Executive achieves this goal by mixing techniques from knowledge-based systems with those of scientific visualization and user-interface design. The intent is to free scientists as much as possible f rom having to deal with the process of doing science so that they can concentrate on the science itself.