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Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
Ag infra kream-presentation-7-6-2013
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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.
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    • 1. Using Knowledge RepresentationModels and Metadata to develop e-science applications for the AgriculturalResearch CommunityGiannis Stoitsisstoitsis@agroknow.grKREAM 2013
    • 2. “meaningful servicesaround high-qualityagricultural data pools”http://www.agroknow.gr
    • 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. • stats• gene banks• gis data• blogs,• journals• open archives• raw data• technologies• learning objects• ………..educators’view
    • 5. • stats• gene banks• gis data• blogs,• journals• open archives• raw data• technologies• learning objects• ………..researchers’view
    • 6. • stats• gene banks• gis data• blogs,• journals• open archives• raw data• technologies• learning objects• ………..practioners’view
    • 7. • stats• gene banks• gis data• blogs,• journals• open archives• raw data• technologies• learning objects• ………..
    • 8. is great…but its not the answer
    • 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. PublisherDate CatalogSubjectIDAuthorTitlewe actually share metadata
    • 11. e.g. an educational resource
    • 12. …metadata reflect the context
    • 13. …sometimes, data also included
    • 14. We need also ontologies andlinked data•stats•gene banks•blogs,•journals•open archives•raw data•learning objects
    • 15. typical problem: computing
    • 16. typical problem: hosting
    • 17. to curate & preserve data we need
    • 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. Case 1: aggregating metadata foragricultural data
    • 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. typically look like this22 Ternier et al., 2010
    • 22. metadata aggregation toolsMore than a harvester: Validation Service Repository Software Registry Service Harvester23Powered by
    • 23. a metadata aggregation workflow that can beported on agINFRAHarvesting Validating TransformingOAI target -XMLsIndexingStoringAutomaticmetadatagenerationDe - duplicationserviceXMLsTriplification
    • 24. Case 2: Setting up SEARCHSERVICE/portal over the cloud
    • 25. Case 3: integrated environments to performresearch experiments
    • 26. agINFRA Cloud/GridRatingsRatingsMonte CarloSimulatorEvaluation of recommendation algorithmsusing grid and cloud infraRecommenderservicesRatingsRatingsRatingsRatingsInfrastructure APIComponentsAPIRefine andtransformImport VisualizePrepare/processEvaluateWeb UI Researchers
    • 27. Integrated environment for evaluatingrecommendation algorithms
    • 28. Case 4: Visualization of researchers’network
    • 29. Case 5: linking germplasm databases andexposing descriptions As linked data
    • 30. Mapping between different metadataformats powered by agINFRA
    • 31. Recommendations and publishingin linked data
    • 32. Case 6: building web based versions ofpublications
    • 33. composite/networked research
    • 34. what researchers need in agINFRA… only a browser and internet connection
    • 35. thank you!stoitsis@agroknow.grwiki.agroknow.grwww.aginfra.eu

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