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CEO:P
-- A Data-Intensive Cyberinfrastructure Component
for Coastal Forecasting and Change Analysis
A National Science Foundation Project under the
Cyberinfrastructure for Environmental Observatories:
Prototype Systems
to Address Cross-Cutting Needs (CEO:P) Program
Researchers at The
Ohio State University
Principal Investigator: Dr. Gagan Agrawal, Professor, Department of Computer Science and
Engineering
Co-Investigator: Dr. Rongxing (Ron) Li, Professor and
Director, Mapping and GIS
Laboratory
Dr. Keith W. Bedford, Professor, The Great Lakes Forecasting System
Dr. Hakan Ferhatosmanoglu,
Professor, Department of Computer
Science and Engineering
Post-Doctoral Researcher and Research Associates:
GIS & Mapping Laboratory: Dr. Xutong Niu and Sagar Deshpande
Computer Science: David Chiu, Qian Zhu and Guadalupe M. Canahuate
The Great Lakes Forecasting System:
Panagiotis Velissariou
Collaborators at
the National Oceanic and Atmospheric Administration (NOAA)
Dr. Frank Aikman, National Ocean
Service (NOS)
Dr. David Schwab, Great Lakes Environmental Research Lab
(GLERL)
Timeline September 2006 - August 2009
Proejct Overview
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 numbers 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.
Firstly, the current systems are very tightly coupled. There is hardly any
reuse of algorithm implementations across different systems. Secondly, it is
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 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.
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 in order
to 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 performed jointly with the National
Oceanic and Atmospheric Administration (NOAA).
The outcomes of
this research will be as follows.
- This research will carry out realistic design,
deployment, and evaluation of cyberinfrastructure.
- In addition, it has the opportunity to impact
the long-term design and operation of a real environmental observation
system.
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, NOAA - National Ocean
Service (NOS), and Dr. David Schwab, NOAA - Great Lakes Environmental
Research Lab (GLERL).
Publications
1.
Li, R., R. Deshpande, X.
Niu, F. Zhou, K. Di, B.
Wu 2008. Assessment of Geopositioning Accuracy in
Integration of Aerial and High-resolution Satellite Imagery and Application
in Shoreline Mapping. Journal of Marine Geodesy, Vol.31, No.3, pp.143-159, doi: 10.1080/01490410802265310
2.
Cheng, K-C., C-Y. Kuo,
C.K. Shum, X. Niu, R. Li, and K.W. Bedford 2008.
Accurate Linking of Lake Erie Water Level
with Shoreline Datum Using GPS Buoy and Satellite Altimetry. Journal of
Terrestrial, Atmospheric and Oceanic Sciences, Vol.19, No.1-2, pp.59-62, doi: 10.3319/TAO.2008.19.1-2.53(SA)
3.
Li, R., S. Deshpande, X. Niu,
I-C. Lee, and B. Wu, 2008. Multi-dimensional
Geospatial Data Integration for Coastal Change Analysis.
International Archives of the Photogrammetry,
Remote Sensing and Spatial Information Sciences, vol. XXXVII, part B8, pp.
1311-1316
4.
Li, R., S. Deshpande, X.
Niu and I-C. Lee 2008. Multi-dimensional Geospatial
Data Integration for Coastal Change Analysis. Abstract, the XXIth ISPRS Congress, Beijing, China,
July 3-11, 2008, 3p
5.
Chiu, D., S. Deshpande,
G. Agrawal and R. Li. 2008 Composing Geoinformatics Workflows with User Preferences. 16th ACM
SIGSPATIAL International Conference on Advances in Geographic Information
Systems (ACM GIS 2008), Irvine,
CA, November 5-7, 2008
For more information,
contact Dr. Rongxing Li at li.282@osu.edu
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