Metadata-powered dissemination of content

1,312 views
1,255 views

Published on

Presentation of cloud-based metadata aggregation infrastructures for agricultural data. Given at Alterra, University of Wageningen, The Netherlands.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,312
On SlideShare
0
From Embeds
0
Number of Embeds
136
Actions
Shares
0
Downloads
10
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • All the services provided to the museums take advantage of the cloud. For instance the interactive installation does not need to have servers that hosts locally the collections and educational material that is used but it connects directly to the infrastructure that runs over the cloud
  • Check the cost of back up for a VM in the US cloud.
  • Check how AJAX is characterized as technology
  • Check how AJAX is characterized as technology
  • Metadata-powered dissemination of content

    1. 1. Metadata-powereddissemination of content Nikos Manouselis nikosm@agroknow.gr
    2. 2. http://wiki.agroknow.gr “meaningful services around high-quality agricultural data pools”
    3. 3. agricultural research(+) content• publications, theses, 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.• …
    4. 4. • stats• gene banks• gis data• blogs,• journals educators’• open archives view• raw data• technologies• learning objects• ………..
    5. 5. • stats• gene banks• gis data• blogs,• journals• open archives• raw data researchers’• technologies view• learning objects• ………..
    6. 6. • stats• gene banks• gis data• blogs,• journals practioners’• open archives view• raw data• technologies• learning objects• ………..
    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. data infrastructure for agriculture• 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
    10. 10. we actually share metadata Author SubjectID Title Publisher Date Catalog
    11. 11. e.g. an educational resource
    12. 12. …metadata reflect the context
    13. 13. …sometimes, data also included
    14. 14. 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 15
    15. 15. typically look like this 16 Ternier et al., 2010
    16. 16. metadata aggregation tools More than a harvester:  Validation Service  Repository Software  Registry Service  Harvester Powered by 17
    17. 17. workflows with commonalitiesHarvesting Validating Transforming OAI target - XMLs Storing Indexing XMLs Automatic De - duplication metadata Triplification service generation
    18. 18. typical problem: computing
    19. 19. typical problem: hosting
    20. 20. to curate & preserve we need
    21. 21. even when machinery exists there are problems• hardware maintenance• technical support• interoperability limitations – no APIs for the dissemination of data across systems• hardware costs
    22. 22. the cloud approach Students Academics Researchers
    23. 23. what can be hosted on the cloud• 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
    24. 24. what data providers need… only a browser and internet connection
    25. 25. CASE 1: DATA MANAGEMENT TOOLOVER THE CLOUD
    26. 26. Educational Pathway Authoring Tool
    27. 27. Educational Pathway Authoring Tool
    28. 28. Cloud service workflow
    29. 29. how it works Demo
    30. 30. comparing costs for hosting datamanagement tool at own site and cloudCloud Hosting at institution•cloud hosting = 20 euros/month •1 server+monitor+ups = 1200 euros•set up effort = 1hr •set up > 1 day effort or 100 euros•back up included •hardware maintenance effort = difficult to be defined but significant•Total for 5 years = 1200 euros •Total for 5 years = 1300 +personnel for hardware maintenance+ costs of unexpected HW breakdowns e.g. supplier, hard disk Costs of software support Costs of software support After 55years the HW should be After years the HW should be could be the same for both could be the same for both renewed/upgraded renewed/upgraded cases cases
    31. 31. CASE 2: SETTING UP SEARCHSERVICE/PORTAL OVER THE CLOUD
    32. 32. demo• GLN backbone (http://www.greenlearningnetwork.com)• Organic.Edunet revamp ( http://www.greenlearningnetwork.com/organicedunet)• AgShare Find OER (http://greenlearningnetwork.com/agshareoer)
    33. 33. how it works Institution Template customization html, css, Ajax, JS Search API Search APIMetadata aggregator for other data types Metadata aggregator for educational content Cloud Data management tool Educational collection management tool
    34. 34. how it works widget in Facebook page Template customization html, css, Ajax, JS Search API Search APIMetadata aggregator for other data types Metadata aggregator for educational content Cloud Data management tool Educational collection management tool
    35. 35. next challenges
    36. 36. 1. Social Research Networking• Connecting peers & visualising social networks, connecting researchers with publications, recommending relevant research – Mendeley (www.mendeley.com), ResearchGate (http://www.researchgate.net), Academia.edu (http://academia.edu), ArnetMiner (http://arnetminer.org), … – Social research components in popular CMSs (JomSocial, Drupal’s Buddylist, Elgg…)
    37. 37. connect peers/publications (+APIs)http://dev.mendeley.com/
    38. 38. extending social CMS componentshttp://voa3r.cc.uah.es
    39. 39. 2. Enriched research objects• Complex, linked research objects – executable scientific workflows, e.g. MyExperiment (http://www.myexperiment.org), Kepler (https://kepler- project.org) – data sets e.g. PLoS (http://www.plos.org), FigShare (http://figshare.com) – processing web services e.g. BioCatalogue (http://www.biocatalogue.org) – Scientist generated classifications/taxonomies e.g. Scratchpads (http://scratchpads.eu) – thematic networks/catalogues e.g. TELeurope (http://www.teleurope.eu), VOA3R (http://voa3r.cc.uah.es)
    40. 40. composite/networked researchhttp://education.natural-europe.eu/green/exhibits/show/grape-cultivars/to-begin-with
    41. 41. 3. End-user interfaces and access• Facilitating and monitoring usage and access – Visualising social bookmarks (Klerx & Duval) – TinyArm (http://atinyarm.appspot.com) – MACE (http://portal.mace-project.eu) and maeve interactive installation at Venice Biennale (http://vimeo.com/1738770)
    42. 42. research visualisations & analytics
    43. 43. interactive navigation interfaces
    44. 44. METADATAAGGREGATOR
    45. 45. thank you! nikosm@agroknow.grhttp://wiki.agroknow.gr http://aginfra.eu

    ×