Ag infra kream-presentation-7-6-2013


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  • 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.
  • Ag infra kream-presentation-7-6-2013

    1. 1. Using Knowledge RepresentationModels and Metadata to develop e-science applications for the AgriculturalResearch CommunityGiannis Stoitsisstoitsis@agroknow.grKREAM 2013
    2. 2. “meaningful servicesaround high-qualityagricultural data pools”
    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 organizationsand 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 andconsumption related to any researchactivity aimed at improvingproductivity and quality of cropsICT for computing, connectivity, storage,instrumentationdata infrastructure for agriculture
    10. 10. PublisherDate CatalogSubjectIDAuthorTitlewe 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 andlinked 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 executedon agINFRA• Data storage & management tools– APIs for content dissemination in large networks• Processing & visualisation tools• Metadata aggregation infra• Search engines and apps for institutions orcommunities• Environments for running experiments e.g.comparing different content recommendationalgorithms
    19. 19. Case 1: aggregating metadata foragricultural data
    20. 20. metadata aggregations• concerns viewing merged collections ofmetadata records from different sources• useful: when access to specific supersets orsubsets of networked collections–records actually stored at aggregator–or queries distributed at virtually aggregatedcollections21
    21. 21. typically look like this22 Ternier et al., 2010
    22. 22. metadata aggregation toolsMore than a harvester: Validation Service Repository Software Registry Service Harvester23Powered by
    23. 23. a metadata aggregation workflow that can beported on agINFRAHarvesting Validating TransformingOAI target -XMLsIndexingStoringAutomaticmetadatagenerationDe - duplicationserviceXMLsTriplification
    24. 24. Case 2: Setting up SEARCHSERVICE/portal over the cloud
    25. 25. Case 3: integrated environments to performresearch experiments
    26. 26. agINFRA Cloud/GridRatingsRatingsMonte CarloSimulatorEvaluation of recommendation algorithmsusing grid and cloud infraRecommenderservicesRatingsRatingsRatingsRatingsInfrastructure APIComponentsAPIRefine andtransformImport VisualizePrepare/processEvaluateWeb UI Researchers
    27. 27. Integrated environment for evaluatingrecommendation algorithms
    28. 28. Case 4: Visualization of researchers’network
    29. 29. Case 5: linking germplasm databases andexposing descriptions As linked data
    30. 30. Mapping between different metadataformats powered by agINFRA
    31. 31. Recommendations and publishingin linked data
    32. 32. Case 6: building web based versions ofpublications
    33. 33. composite/networked research
    34. 34. what researchers need in agINFRA… only a browser and internet connection
    35. 35. thank you!