Knowledge-based Environmental Data AnalysisEPA Grant Number: R825199
Title: Knowledge-based Environmental Data Analysis
Investigators: Fine, Steven S. , Smith, W. Ted , Thorpe, Steven R. , Wheeler, Neil J.M.
Current Investigators: Fine, Steven S.
Institution: MCNC / North Carolina Supercomputing Center
EPA Project Officer: Hahn, Intaek
Project Period: October 29, 1996 through October 28, 1999 (Extended to October 27, 2000)
Project Amount: $597,994
RFA: High Performance Computing (1996) RFA Text | Recipients Lists
Research Category: Health , Ecosystems , Environmental Statistics
Description:Existing environmental visualization packages provide many useful features, but they address only part of a user's data analysis needs. For most users, visualizations and other analyses are simply a means to an end, such as developing a policy or an improved model. Unfortunately, there is a very large gap between a user's goal and the support available tools provide. Currently users have to cross that gap, adapting their data analysis approach to conform to the existing visualization packages.
The investigators propose to fill the gap from the other direction by developing techniques that will more closely match the users' approaches to analysis. By using knowledge about users, their work, environmental processes, and existing analysis tools, it is possible to develop new ways of looking at analyses that provide the comprehensive support for data analysis that scientists and managers need to efficiently and effectively understand the increasingly complex environmental issues that challenge society.
The investigators will develop techniques for a Knowledge-Based Environmental Data Analysis Assistant. This Assistant will provide an intelligent interface between the user and existing analysis packages and will provide support at a higher conceptual level for creating and managing analyses. The Assistant will use its knowledge to suggest appropriate analyses (e.g., contour ozone fields after running an oxidant model), to provide convenient access to previous analyses, and to utilize existing packages.
To accomplish this the investigators will develop techniques to intelligently support the creation and management of analyses, to utilize existing analysis packages, and to explain the Assistant's actions. To limit the scope of the work, the research will focus on data analysis techniques that support the evaluation of results from several types of models. The research team will create a conceptual model of environmental model evaluation that will catalog knowledge that can be incorporated into the Assistant. The conceptual model will serve as the basis for the development of a software testbed for the knowledge-based techniques. The knowledge about the users, their work, environmental processes, and analysis tools will drive the Assistant's suggestions and responses to the user. The investigators will also develop a graphical user interface that will provide powerful capabilities for creating and managing analyses. To validate the new techniques, modelers will be consulted frequently to obtain feedback on approaches.
The proposed techniques will provide a number of benefits to the environmental community. They will allow modelers to focus better on their primary goals by eliminating many mundane, repetitive, and distracting operations currently required by analysis tools. The techniques will also help modelers gain better insight by suggesting appropriate analyses that might not have been considered (e.g., capabilities provided by a specialized tool). Utilization of high performance computing resources will increase because the Assistant will delegate tasks to the most efficient source of services.