Skip to main content

CEO:P--A Data-Intensive Cyberinfrastructure Component for Coastal Forecasting and Change Analysis

Abstract OCI 0619041<br/><br/>Over the years, much work has been done on observing and modeling the environment. Many complex systems have been, or are being, built. Despite advances in the amount of data being collected, (including larger number of sources as well as increased spatio-temporal granularity) and enhancements in the techniques being used for analyzing these datasets, a number of challenges remain in this area. <br/><br/>Firstly, the current systems are very tightly coupled. There is hardly any reuse of algorithm implementations across different systems. It is also extremely hard to test or incorporate new analysis algorithms. The implementations are closely tied to the available resources, and finally, the existing systems cannot adapt the granularity of analysis to the resource availability and time constraints. The emerging trend towards (closely related) concepts of service-oriented architectures and grid computing can alleviate the above problems. They can enable development of services that are not tied to specific datasets or end applications, and implementation of applications using these services. However, this also requires advances in grid middleware components that are able to support streaming applications and data virtualization/integration.<br/><br/>This project proposes to develop and evaluate a cyberinfrastructure component for environmental applications. This will include developments in middleware, model integration, analysis, and mining techniques, and the use of a service model for supporting two closely related applications. These applications will be real-time coastal now casting and forecasting, and long term coastal erosion analysis and prediction. The specific problems addressed are as follows. In the first application, focus will be on real-time now casting and forecasting of coastal conditions. Middleware and service-oriented implementation will be used to allow new algorithms to be inserted (for example, for beach closings and coliform forecasts), allow more complex models to be used based on resource and time constraints, allow new data streams to be inserted flexibly, and allow new algorithms for analysis and interpretation to be operated on data being produced from forecasting/now casting models. In the second application, advanced models will be developed for long-term coastal changes and erosion patterns, and allow larger scale, distributed, and flexible data analysis. Implementation and evaluation will be in the context of the Great Lakes Observing System (GLOS) and will be done jointly with the National Oceanic and Atmospheric Administration (NOAA). This is an excellent opportunity to carry out realistic<br/>design, deployment, and evaluation of the cyberinfrastructure component, and also impact the long-term design and operation of a real environmental observation system.<br/>This project will be a joint effort between The Ohio State University (OSU) and the National Oceanic and Atmospheric Administration (NOAA). The OSU team includes two computer science researchers: Gagan Agrawal (grid middleware systems) and Hakan Ferhatosmanoglu (databases and data analysis), and two environmental researchers: Keith Bedford (environmental modeling) and Ron Li (geospatial data analysis and remote sensing). The NOAA collaborators include Dr. Frank Aikman,<br/>NOAA-National Ocean Service (NOS), and Dr. David Schwab, NOAA -Great Lakes Environmental Research Lab (GLERL).

In progress
Project URL
Project Database
Start Date
End Date

The Great Lakes - St. Lawrence Research Inventory is an
interactive, Internet-based, searchable database created as a tool to collect and disseminate
up-to-date information about research projects in the
Great Lakes - St. Lawrence Region.