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Application Scenario: Smoke Impact REASoN Project:  Application of NASA ESE Data and Tools to Particulate Air Quality Management   ( PPT/PDF ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Record Smoke Impact on PM  Concentrations [email_address] ,  stefan @me.wustl. edu Smoke  Event
Web Services for Air Quality Management
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, serendipitous  and 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 data  Most tools are personal, dataset specific and ‘hand made’ Analysis tools for data browsing, fusion and  data/model integration Web services  for data registration, geo-time-parameter referencing,  non-intrusive addition of  ad hoc  data; 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 some  surface monitoring data, Limited real-time data downloaded from providers, extracted, geo-time-param-coded, etc. by each analyst  Real-time access to routine and  ad-hoc  fire, smoke, transport data/ and models How to get there New capabilities Current state IT need vision
Project Domain, New Technologies and Barriers ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Flow & Processing in AQ Management ,[object Object],AQ DATA EPA Networks IMPROVE Visibility  Satellite-PM Pattern  METEOROLOGY Met. Data  Satellite-Transport  Forecast model EMISSIONS National Emissions  Local Inventory  Satellite Fire Locs Status and Trends AQ Compliance Exposure Assess. Network Assess. Tracking Progress AQ  Management  Reports ‘ Knowledge’ Derived from Data Primary Data  Diverse Providers Data ‘Refining’ Processes  Filtering, 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
A Wrapper Service: TOMS Satellite Image Data ,[object Object],[object Object],[object Object],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
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-specific  wrappers Abstract Data Abstract data slices are requested by viewers; uniform data are delivered by  wrapper  services DataView 2 DataView 3 View Data Processed data are delivered to the user as multi-layer views by  portrayal and overlay web services Processed Data Data passed through filtering, aggregation, fusion and other  processing web  services
Service Oriented Architecture: Data AND Services are Distributed  ,[object Object],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 output  User Carries less Burden In service-oriented peer-to peer architecture, the user is aided by software ‘agents’ Control  Data Process Process Process Data Service Catalog Process Chain 2 Chain 1 Chain 3 Data Service
An Application Program: Voyager Data Browser ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data Sources Controls Displays I/O Layer Device Drivers Wrappers App State Data Flow Interpreter Core Web Services WSDL Ports

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Ws For Aq

  • 1.
  • 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, serendipitous and 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 data Most tools are personal, dataset specific and ‘hand made’ Analysis tools for data browsing, fusion and data/model integration Web services for data registration, geo-time-parameter referencing, non-intrusive addition of ad hoc data; 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 some surface monitoring data, Limited real-time data downloaded from providers, extracted, geo-time-param-coded, etc. by each analyst Real-time access to routine and ad-hoc fire, smoke, transport data/ and models How to get there New capabilities Current state IT need vision
  • 4.
  • 5.
  • 6.
  • 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-specific wrappers Abstract Data Abstract data slices are requested by viewers; uniform data are delivered by wrapper services DataView 2 DataView 3 View Data Processed data are delivered to the user as multi-layer views by portrayal and overlay web services Processed Data Data passed through filtering, aggregation, fusion and other processing web services
  • 8.
  • 9.