Cyberinfrastructure for Coastal Forecasting and Change Analysis

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Presentation transcript:

Cyberinfrastructure for Coastal Forecasting and Change Analysis Gagan Agrawal Hakan Ferhatosmanoglu Xutong Niu Ron Li Keith Bedford

Coastal Forecasting and Change Detection (Lake Erie) Lake Erie Coastal Erosion Analysis: OSU Geodetic Science and ODNR GLOS: Collaboration between OSU Civil and NOAA

Proposed Infrastructure and Collaboration

Project Premise and Challenges Limitation of Current Environmental Observation Systems Tightly coupled systems No reuse of algorithms Very hard to experiment with new algorithms Closely tied to existing resources Our claim Emerging trends towards web-services and grid-services can help Challenges Existing Grid Middleware Systems have not considered streaming data or data integration issues Enabling algorithms (data mining, query planning, data fusion) need to be implemented as grid/web-services

Middleware Developed at Ohio State Automatic Data Virtualization Framework Enabling processing and integration of data in low-level formats GATES (Grid-based AdapTive Execution on Streams) Processing of distributed data streams FREERIDE-G (FRamework for Rapid Implementation of Datamining Engines in Grid) Supporting scalable data analysis on remote data

Application Details: Coastal Erosion Prediction and Analysis Focus: Erosion along Lake Erie Shore Serious problem Substantial Economic Losses Prediction requires data from Variety of Satellites In-situ sensors Historical Records Challenges Analyzing distributed data Data Integration/Fusion Long Term Goal : Create Service-oriented implementation Design a WSDL to describe available data Describe available tools and services Support discovery and composition of datasets and services for a given query

Application Details: Great Lakes Now/ForeCasting GLOS: Great Lakes Observing System Co-designer/project manager: K. Bedford, a co-PI on this project Collaboration with NOAA Limitations: Hard-wired Cannot incorporate new streams or algorithms Create a Demand-driven Implementation using GATES Event of Interest A boat accident, oil leakage Need to run a new model Time Constraints Find grid resources on the fly Need to decide: Spatial and Temporal Granularity Parameters to Model