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http://capitawiki.wustl.edu/index.php/20050119_Application_Scenario:_Smoke_Impact
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Record Smoke Impact on PMConcentrations [email_address] ,stefan @me.wustl. edu SmokeEvent 2. Web Services for Air Quality Management 3. IT needs and Capabilities:Web Services Community interaction during events through virtual workgroup sites; quantitative now-casting and observation-augmented forecasting Smoke event summary and forecast suitably packaged and delivered for agency and public decision makers Uncoordinated event monitoring, serendipitousand limited analysis. Event summary by qualitative description and illustration Smoke event summary and forecast for managers (air quality, aviation safety) and the public Services linking tools Service chaining languages for building web applications; Data browsers, data processing chains;Tools for navigating spatio-temporal data;User-defined views of the smoke; Conceptual framework for merging satellite, surface and modeling dataMost tools are personal, dataset specific and hand made Analysis tools for data browsing, fusion anddata/model integration Web servicesfor data registration, geo-time-parameter referencing,non-intrusive addition ofad hocdata; communal tools for data finding, extracting Agents (services) to seamlessly access distributed data and provide uniformly presented views of the smoke.Human analysts access a fraction of a subset of qualitative satellite images and somesurface monitoring data, Limited real-time data downloaded from providers, extracted, geo-time-param-coded, etc. by each analystReal-time access to routine andad-hocfire, smoke, transport data/ and models How to get there New capabilities Current state IT need vision 4. Project Domain, New Technologies and Barriers
5. Data Flow & Processing in AQ Management
AQ DATA EPA Networks IMPROVE VisibilitySatellite-PM PatternMETEOROLOGY Met. DataSatellite-TransportForecast model EMISSIONS National EmissionsLocal InventorySatellite Fire Locs Status and Trends AQ Compliance Exposure Assess. Network Assess. Tracking Progress AQManagementReports Knowledge Derived from Data Primary DataDiverse Providers Data Refining ProcessesFiltering, Aggregation, Fusion Driving Forces :Provider Push User Pull Information Engineering: Info driving forces, source-transformer-sink nodes, processes (services) in each node, flow & other impediments, overall systems modeling and analysis 6. A Wrapper Service: TOMS Satellite Image Data
src_img_width src_img_height src_margin_right src_margin_left src_margin_top src_margin_bottom src_lon_min src_lat_max src_lat_min src_lon_max Image Description for Data Access: src_image_width=502 src_image_height=329 src_margin_bottom=105 src_margin_left=69 src_margin_right=69 src_margin_top=46 src_lat_min=-70 src_lat_max=70 src_lon_min=-180 src_lon_max=180 The daily TOMS images (virtually no metadata) reside on the FTP archive, e.g.ftp://toms. gsfc . nasa . gov /pub/ eptoms /images/aerosol/Y2000/IM_ aersl _ ept _20000820. png URL template:ftp://toms.gsfc.nasa.gov/pub/eptoms/images/aerosol/y[yyyy]/IM_aersl_ept_[yyyy][mm][dd].png Transparent colors for overlays RGB(89,140,255) RGB(41,117,41) RGB(23,23,23) RGB(0,0,0) ttp ://capita.wustl. edu / dvoy _2.0.0/ dvoy _services/ cgi . wsfl ?view_state= TOMS_AI&lat_min=0&lat_max=70& lon _min=-180& lon _max=-60&datetime=2001-04-13&image_width=800&image_height=500 http://capita.wustl. edu / dvoy _2.0.0/ dvoy _services/ cgi . wsfl ?view_state= NAAPS_GLO_DUST_AOT&lat_min=0&lat_max=70& lon _min=-180& lon _max=-60&datetime=2001-04-13&image_width=800&image_height=500 http://capita.wustl. edu / dvoy _2.0.0/ dvoy _services/ cgi . wsfl ?view_state= VIEWS_Soil&lat_min=0&lat_max=70& lon _min=-180& lon _max=-60&datetime=2001-04-13&image_width=800&image_height=500 7. Generic Data Flow and Processing for Browsing DataView 1 Data Processed Data Portrayed Data Process Data Portrayal/ Render Abstract Data Access View Wrapper Physical Data Abstract Data Physical Data Resides in autonomous servers; accessed non-intrusively by data and view-specificwrappers Abstract Data Abstract data slices are requested by viewers; uniform data are delivered bywrapperservices DataView 2 DataView 3 View Data Processed data are delivered to the user as multi-layer views byportrayal and overlay web services Processed Data Data passed through filtering, aggregation, fusion and otherprocessing webservices 8. Service Oriented Architecture: Data AND Services are Distributed
Data, as well as services and users (of data and services) are distributed Users compose data processing chains form reusable services Intermediate and resulting data are also exposed for possible further use Processing chains can be further linked into complex value-adding data refineries Service chain representation User Tasks: Fi nd data and services Compose service chains Expose outputUser Carries less Burden In service-oriented peer-to peer architecture, the user is aided by software agents ControlData Process Process Process Data Service Catalog Process Chain 2 Chain 1 Chain 3 Data Service 9. An Application Program: Voyager Data Browser
Data Sources Controls Displays I/O Layer Device Drivers Wrappers App State Data Flow Interpreter Core Web Services WSDL Ports