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The MELODIES project: Exploiting Linked Open Geospatial Data

The MELODIES project: Exploiting Linked Open Geospatial Data. Jon Blower University of Reading o n behalf of MELODIES consortium. MELODIES Overview. Maximizing the Exploitation of Linked Open Data In Enterprise and Science 3 years, started 1 st November 2013 16 partners in 8 EU countries

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The MELODIES project: Exploiting Linked Open Geospatial Data

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  1. The MELODIES project:Exploiting Linked Open Geospatial Data Jon Blower University of Reading on behalf of MELODIES consortium

  2. MELODIES Overview • Maximizing the Exploitation of Linked Open Data In Enterprise and Science • 3 years, started 1st November 2013 • 16 partners in 8 EU countries • €6.7M budget • Aims to demonstrate the business and scientific benefits of releasing data openly • Responds to European Open Data Strategy • Sister project of SWITCH-ON and SmartOpenData

  3. MELODIES project consortium

  4. MELODIES Overview

  5. The MELODIES Services Site-specific information for land management Improving Emission Inventories Assessment of Good Environmental Status for oceans Urban Ecosystem Accounting Marine transport Desertification indicators Groundwater modelling Customised crisis, disaster and risk mapping

  6. Anatomy of typical MELODIES service(there is a lot of variation on this theme!) EO Data processing Discrete features (RDF) Earth observation Imagery (raster) Linked Data integration Linked Data sources e.g. Population data, CORINE land cover User interface (WebGIS) driven by SPARQL queries Contextual information e.g. Maps, Administrative boundaries

  7. Some Technologies We Are Using • Terraduecloud platform and sandbox environment • Building on GeoWOW • Strabonsemantic spatiotemporal RDF store • University of Athens • Building on TELEIOS • Web Mapping, OGC web services and portal tools • Building on MyOcean, GeoViQua, more…

  8. Challenges and questions • We understand Linked Data from publisher’s point of view, what about consumer’s? • How to join RDF data sources together? • Where do I do my analysis? • What tools can we use? • How to handle large raster volumes (e.g. EO data)? • Currently we keep images as-is but record metadata and extracted features as Linked Data • What is best approach for publishing commercial or restricted data? • Perhaps publish metadata and provenance? • How do we handle time-varying data? • We will use stRDF and stSPARQL • Use Linked Data to enable user annotation of datasets? • Cf. CHARMe project (http://www.charme.org.uk)

  9. Thank you!j.d.blower@reading.ac.uk@Jon_Blowerhttp://melodiesproject.eu@MelodiesProject

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