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Web Services: ES Rationale and Assertions ,[object Object],[object Object],[object Object],[object Object],Provider Push Science Pull Flow of Data Flow of Control DATA 1 Data 2 Data 2 Knowledge Prod. 1 Knowledge Prod. 2 Knowledge Prod. 4 Knowledge Prod. 3 Knowledge Prod. 5 Web Services for Refining Data to Knowledge
[Better Earth-Sun] Science is the DRIVER!
The Researcher’s Challenge “ The researcher cannot get  access  to the data; if he can, he cannot  read  them; if he can read them, he does not know  how good  they are; and if he finds them good he cannot  merge  them with other data.” Information Technology and the Conduct of Research: The Users View National Academy Press, 1989 ,[object Object],[object Object],[object Object],[object Object],[object Object]
OGC & OpenDAP are addressing the Finding and Reformatting tasks The  custom processing  of data into knowledge is still a major burden
Earth Science Data to Knowledge Transformation: Value-Adding Processes  Petabytes 10 15 Terabytes 10 12 Gigabytes 10 9 Megabytes  10 6 Calibration, Transformation To Characterized Geophysical Parameters Filtering, Aggregation, Fusion, Modeling, Trends, Forecasting  Interactive Dissemination ACCESS Multi-platform/parameter, high space/time resolution, remote & in-situ sensing Sensing Analysis & Synthesis Data Acquisition Value Chain (Network) InfoSystem Goal: Add as much value to the data as possible to benefit all users Data Usage Value Network Flexible data selection, and processing to to deliver right knowledge, right place right time Data - L1 Information – L2 Knowledge – L3-6? Usable Knowledge Query Data Distributed, Dynamic More Local, DAAC Processing Knowledge Use
Value-Added Processing in Service Oriented Architecture ,[object Object],Data, services and users are distributed throughout the network Users compose data processing chains form reusable services Intermediate data are also exposed for possible further use Chains can be linked to form compound value-adding processes Service chain representation User Tasks: Fi nd data and services Compose service chains Expose output  Chain 2 Chain 1 Chain 3 Data Service User Carries less Burden In service-oriented peer-to peer architecture, the user is aided by software ‘agents’ Control  Data Chain 1 Chain 2 Chain 3 Data Service Catalog User
Data Flow and Flow Control in AQ Management ,[object Object],[object Object],[object Object],Provider Push User Pull Flow of Data Flow of Control AQ DATA METEOROLOGY EMISSIONS DATA Informing Public AQ Compliance Status and Trends Network Assess. Tracking Progress Data to Knowledge ‘Refinery’
Data Flow and Flow Control in AQ Management ,[object Object],States Regions AIRS AQS EPA Air Portal EPA Science Portal VIEWS AIRNOW
Data Flow and Flow Control in AQ Management ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
A Sample of Datasets Accessible through DataFed/ESIP Mediation Near Real Time (~ day) ,[object Object],MODIS Reflectance MODIS AOT TOMS Index GOES  AOT GOES  1km Reflec NEXTRAD Radar MODIS  Fire Pix NRL MODEL NWS Surf Wind, Bext
Assertions on Web Services Technology ,[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]
Interoperability Stack ,[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],HTTP, SMTP Addressing, Data flow Transport XML Data format Syntax SOAP, WS-* ext. Communication behavior Protocol Schema, WSDL Types Data WSDL ext., Policy, RDF Meaning Semantics Standards Description Layer
Value Network ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Value Network ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Strategy Tradeoffs in the Knowledge and Network Economy ,[object Object],[object Object],[object Object],[object Object],[object Object]

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2005-03-29 Web Services: ES Rationale and Assertions

  • 1.
  • 2. [Better Earth-Sun] Science is the DRIVER!
  • 3.
  • 4. OGC & OpenDAP are addressing the Finding and Reformatting tasks The custom processing of data into knowledge is still a major burden
  • 5. Earth Science Data to Knowledge Transformation: Value-Adding Processes Petabytes 10 15 Terabytes 10 12 Gigabytes 10 9 Megabytes 10 6 Calibration, Transformation To Characterized Geophysical Parameters Filtering, Aggregation, Fusion, Modeling, Trends, Forecasting Interactive Dissemination ACCESS Multi-platform/parameter, high space/time resolution, remote & in-situ sensing Sensing Analysis & Synthesis Data Acquisition Value Chain (Network) InfoSystem Goal: Add as much value to the data as possible to benefit all users Data Usage Value Network Flexible data selection, and processing to to deliver right knowledge, right place right time Data - L1 Information – L2 Knowledge – L3-6? Usable Knowledge Query Data Distributed, Dynamic More Local, DAAC Processing Knowledge Use
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.  
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.

Notas do Editor

  1. AQ data arise from diverse sources, each having specific history, driving forces, formats, quality, etc. Data analysis, i.e. turning the raw data into ‘actionable’ knowledge, requires combining data from these sources The three major data ‘processing’ operations (services) are filtering, aggregation and fusion