4 Project Premise and Challenges Limitation of Current Environmental Observation SystemsTightly coupled systemsNo reuse of algorithmsVery hard to experiment with new algorithmsClosely tied to existing resourcesOur claimEmerging trends towards web-services and grid-services can helpChallengesExisting Grid Middleware Systems have not considered streaming data or data integration issuesEnabling algorithms (data mining, query planning, data fusion) need to be implemented as grid/web-services
5 Middleware Developed at Ohio State Automatic Data Virtualization FrameworkEnabling processing and integration of data in low-level formatsGATES (Grid-based AdapTive Execution on Streams)Processing of distributed data streamsFREERIDE-G (FRamework for Rapid Implementation of Datamining Engines in Grid)Supporting scalable data analysis on remote data
6 Application Details: Coastal Erosion Prediction and Analysis Focus: Erosion along LakeErie ShoreSerious problemSubstantial Economic LossesPrediction requires data fromVariety of SatellitesIn-situ sensorsHistorical RecordsChallengesAnalyzing distributed dataData Integration/FusionLong Term Goal : Create Service-oriented implementationDesign a WSDL to describe available dataDescribe available tools and servicesSupport discovery and composition of datasets and services for a given query
7 Application Details: Great Lakes Now/ForeCasting GLOS: Great Lakes Observing SystemCo-designer/project manager: K. Bedford, a co-PI on this projectCollaboration with NOAALimitations: Hard-wiredCannot incorporate new streams or algorithmsCreate a Demand-driven Implementation using GATESEvent of InterestA boat accident, oil leakageNeed to run a new modelTime ConstraintsFind grid resources on the flyNeed to decide:Spatial and Temporal GranularityParameters to Model
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