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Ag infra kream-presentation-7-6-2013

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Ag infra kream-presentation-7-6-2013

  1. 1. Using Knowledge Representation Models and Metadata to develop e- science applications for the Agricultural Research Community Giannis Stoitsis stoitsis@agroknow.gr KREAM 2013
  2. 2. “meaningful services around high-quality agricultural data pools” http://www.agroknow.gr
  3. 3. • publications, thesis, reports, other grey literature • educational material and content, courseware • primary data, such as measurements & observations – structured, e.g. datasets as tables – digitized, e.g. images, videos • secondary data, such as processed elaborations – e.g. dendrograms, pie charts, models • provenance information, incl. authors, their organizations and projects • experimental protocols & methods • social data, tags, ratings, etc. • … agricultural research(+) content
  4. 4. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. educators’ view
  5. 5. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. researchers’ view
  6. 6. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ……….. practioners’ view
  7. 7. • stats • gene banks • gis data • blogs, • journals • open archives • raw data • technologies • learning objects • ………..
  8. 8. is great …but its not the answer
  9. 9. • aim is: promoting data sharing and consumption related to any research activity aimed at improving productivity and quality of crops ICT for computing, connectivity, storage, instrumentation data infrastructure for agriculture
  10. 10. Publisher Date Catalog Subject ID Author Title we actually share metadata
  11. 11. e.g. an educational resource
  12. 12. …metadata reflect the context
  13. 13. …sometimes, data also included
  14. 14. We need also ontologies and linked data • stats • gene banks • blogs, • journals • open archives • raw data • learning objects
  15. 15. typical problem: computing
  16. 16. typical problem: hosting
  17. 17. to curate & preserve data we need
  18. 18. what can be hosted and executed on agINFRA • Data storage & management tools – APIs for content dissemination in large networks • Processing & visualisation tools • Metadata aggregation infra • Search engines and apps for institutions or communities • Environments for running experiments e.g. comparing different content recommendation algorithms
  19. 19. Case 1: aggregating metadata for agricultural data
  20. 20. metadata aggregations • concerns viewing merged collections of metadata records from different sources • useful: when access to specific supersets or subsets of networked collections –records actually stored at aggregator –or queries distributed at virtually aggregated collections 21
  21. 21. typically look like this 22 Ternier et al., 2010
  22. 22. metadata aggregation tools More than a harvester:  Validation Service  Repository Software  Registry Service  Harvester 23 Powered by
  23. 23. a metadata aggregation workflow that can be ported on agINFRA Harvesting Validating Transforming OAI target - XMLs IndexingStoring Automatic metadata generation De - duplication service XMLs Triplification
  24. 24. Case 2: Setting up SEARCH SERVICE/portal over the cloud
  25. 25. Case 3: integrated environments to perform research experiments
  26. 26. agINFRA Cloud/Grid RatingsRatings Monte Carlo Simulator Evaluation of recommendation algorithms using grid and cloud infra Recommender servicesRatingsRatingsRatingsRatings Infrastructure API Components API Refine and transform Import Visualize Prepare/pr ocess Evaluate Web UI Researchers
  27. 27. Integrated environment for evaluating recommendation algorithms
  28. 28. Case 4: Visualization of researchers’ network
  29. 29. Case 5: linking germplasm databases and exposing descriptions As linked data
  30. 30. Mapping between different metadata formats powered by agINFRA
  31. 31. Recommendations and publishing in linked data
  32. 32. Case 6: building web based versions of publications
  33. 33. composite/networked research
  34. 34. what researchers need in agINFRA … only a browser and internet connection
  35. 35. thank you! stoitsis@agroknow.gr wiki.agroknow.gr www.aginfra.eu

Editor's Notes

  • Here we present the architecture of such an environment and the proposed software stack Monte Carlo will be a separate component that can run also on the Grid and that will br provided through an API. The API will be documented.

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