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Using Knowledge Representation
Models and Metadata to develop e-
science applications for the Agricultural
Research Community
Giannis Stoitsis
stoitsis@agroknow.gr
KREAM 2013
“meaningful services
around high-quality
agricultural data pools”
http://www.agroknow.gr
• 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
• stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
educators’
view
• stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
researchers’
view
• stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
practioners’
view
• stats
• gene banks
• gis data
• blogs,
• journals
• open archives
• raw data
• technologies
• learning objects
• ………..
is great
…but its not the answer
• 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
Publisher
Date Catalog
Subject
ID
Author
Title
we actually share metadata
e.g. an educational resource
…metadata reflect the context
…sometimes, data also included
We need also ontologies and
linked data
•
stats
•
gene banks
•
blogs,
•
journals
•
open archives
•
raw data
•
learning objects
typical problem: computing
typical problem: hosting
to curate & preserve data we need
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
Case 1: aggregating metadata for
agricultural data
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
typically look like this
22 Ternier et al., 2010
metadata aggregation tools
More than a harvester:
 Validation Service
 Repository Software
 Registry Service
 Harvester
23
Powered by
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
Case 2: Setting up SEARCH
SERVICE/portal over the cloud
Case 3: integrated environments to perform
research experiments
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
Integrated environment for evaluating
recommendation algorithms
Case 4: Visualization of researchers’
network
Case 5: linking germplasm databases and
exposing descriptions As linked data
Mapping between different metadata
formats powered by agINFRA
Recommendations and publishing
in linked data
Case 6: building web based versions of
publications
composite/networked research
what researchers need in agINFRA
… only a browser and internet connection
thank you!
stoitsis@agroknow.gr
wiki.agroknow.gr
www.aginfra.eu

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Ag infra kream-presentation-7-6-2013

Hinweis der Redaktion

  1. 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.