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Large-scale (meta)Data Aggregators & Infrastructure Requirements the case of agriculture Nikos Manouselis Agro-Know Technologies & ARIADNE Foundation

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Presentation on theme: "Large-scale (meta)Data Aggregators & Infrastructure Requirements the case of agriculture Nikos Manouselis Agro-Know Technologies & ARIADNE Foundation"— Presentation transcript:

1 Large-scale (meta)Data Aggregators & Infrastructure Requirements the case of agriculture Nikos Manouselis Agro-Know Technologies & ARIADNE 2012, Dubai, 13/12/12

2 Publications, theses, reports, other grey literature Educational material and content, courseware Primary data: – Structured, e.g. datasets as tables – Digitized : images, videos, etc. Secondary data (elaborations, e.g. a dendogram) Provenance information, incl. authors, their organizations and projects Experimental protocos & methods Social data, tags, ratings, etc. (agricultural) research data

3 stats gene banks gis data blogs, journals open archives raw data technologies learning objects ……….. educators view

4 stats gene banks gis data blogs, journals open archives raw data technologies learning objects ……….. researchers view

5 stats gene banks gis data blogs, journals open archives raw data technologies learning objects ……….. practioners view

6 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

7 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

8 Publisher DateCatalog Subject ID Author Title we actually share metadata

9 e.g. an educational resource

10 …metadata reflect the context

11 …sometimes, data also included

12 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 12

13 typically look like this 13 Ternier et al., 2010

14 typical problem: computing

15 typical problem: hosting

16 an ideal scenario

17 Data provider in need of hosting & storage of small- scale CMS sets up own CMS instance Data provider in need of large scale hosting & replication CMS requests space/accounts in large-scale CMS Data provider hosting CMS at own or external/commercial infrastructure interested to expose (meta)data to e- infrastructure register as data source hosted over cloud computed over grid

18 shares (meta)data e.g. through OAI-PMH indexed & available through CIARD RING shares (meta)data e.g. through OAI-PMH (META)DATA AGGREGATOR supported by scientific gateway computed & hosted over agINFRA grid/cloud computed over grid & hosted over cloud

19 computed over grid computed over grid & hosted over cloud …

20 its all about efficient metadata management storage issues: where components are hosted, how metadata aggregations & their versions handled/stored, scaling up computing issues: harvesting takes time/resources and needs to be invoked often, automatic tagging tasks demanding often recurring, similar workflows are needed (validate, transform, harvest, auto-tag, index) overall need

21 why should you care?

22 promoting course descriptions 22 push course information to various syndication/aggregation sites to allow users discover them – OCW search engine (http://www.ocwsearch.com)http://www.ocwsearch.com – Moodle Hub concept (hub.moodle.org)

23 including relevant content 23 allow course creator/author to find relevant material and resources to enrich course – Europeana ingestion widget (http://wiki.agroknow.gr/agroknow/index.php/Hack4Euro pe_2012)http://wiki.agroknow.gr/agroknow/index.php/Hack4Euro pe_2012 suggest to learners additional courses and material relevant to what they access – Eummenas Moodle Widget (http://www.eummena.org/index.php/labs)http://www.eummena.org/index.php/labs

24 developing more end-user services 24 Web portals to support user communities (e.g. thematic, geographical, social, cultural) – MACE portal (http://portal.mace-project.eu)http://portal.mace-project.eu – Photodentro Greek school collections portal (http://photodentro.edu.gr)http://photodentro.edu.gr – VOA3R social platform for researchers (http://voa3r.cc.uah.es)http://voa3r.cc.uah.es

25 wrap up

26 (META)DATA AGGREGATOR

27 considerations easily replicated cloud-hosted software applications (e.g. DSPACE instances) portal/service owners and software developers to use the infrastructure as a basis power up existing data & service networks

28 interesting: TERENA OER pilot interconnecting open educational resource repositories of NRENs https://confluence.terena.org/pages/viewpage.action?pageId=

29 interesting: GLOBE Global Learning Objects Brokering Exchange Alliance

30 thank you!


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