skip to main content
10.1145/3269206.3269232acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

From Copernicus Big Data to Big Information and Big Knowledge: A Demo from the Copernicus App Lab Project

Published:17 October 2018Publication History

ABSTRACT

Copernicus is the European program for monitoring the Earth. It consists of a set of complex systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data collected by Copernicus is made available freely following an open access policy. Information extracted from Copernicus data is disseminated to users through the Copernicus services which address six thematic areas: land, marine, atmosphere, climate, emergency and security. We present a demo from the Horizon 2020 Copernicus App Lab project which takes big data from the Copernicus land service, makes it available on the Web as linked geospatial data and interlinks it with other useful public data to aid the development of applications by developers that might not be Earth Observation experts. Our demo targets a scenario where we want to study the "greenness" of Paris.

References

  1. K. Bereta, H. Caumont, E. Goor, M. Koubarakis, D.-A. Pantazi, G. Stamoulis, S. Ubels, V. Venus, and F. Wahyudi. 2018. From Big Data to Big Information and Big Knowledge: the Copernicus App Lab Project. In International Conference on Information and Knowledge Management (CIKM 2018), Case study/Industry paper. Submitted. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Konstantina Bereta and Manolis Koubarakis. 2016. Ontop of Geospatial Databases. In Proceedings of the 15th International Semantic Web Conference.Google ScholarGoogle ScholarCross RefCross Ref
  3. Y. Chronis, Y. Foufoulas, V. Nikolopoulos, A. Papadopoulos, L. Stamatogiannakis, C. Svingos, and Y. E. Ioannidis. 2016. A Relational Approach to Complex Dataflows. In Proceedings of the EDBT/ICDT Workshops 2016, Bordeaux, France.Google ScholarGoogle Scholar
  4. Souripriya Das, Seema Sundara, and Richard Cyganiak. 2012. R2RML: RDB to RDF Mapping Language. W3C Rec.. (2012). Available from: http://www.w3.org/TR/r2rml/.Google ScholarGoogle Scholar
  5. Anastasia Dimou, Miel Vander Sande, Pieter Colpaert, Ruben Verborgh, Erik Mannens, and Rik Van de Walle. 2014. RML: a generic language for integrated RDF mappings of heterogeneous data. In LDOW.Google ScholarGoogle Scholar
  6. George Garbis, Kostis Kyzirakos, and Manolis Koubarakis. 2013. Geographica: A Benchmark for Geospatial RDF Stores. In the 12th International Semantic Web Conference, Sydney, Australia, October 21-25, 2013, Proceedings. 343--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Koubarakis, K. Bereta, G. Papadakis, D. Savva, and G. Stamoulis. 2017. Big, Linked Geospatial Data and Its Applications in Earth Observation. IEEE Internet Computing, Vol. July/August (2017), 87--91.Google ScholarGoogle Scholar
  8. Manolis Koubarakis and Kostis Kyzirakos. 2010. Modeling and Querying Metadata in the Semantic Sensor Web: The Model stRDF and the Query Language stSPARQL. In ESWC (LNCS), Vol. 6088. Springer, 425--439. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Kostis Kyzirakos, Ioannis Vlachopoulos, Dimitrianos Savva, Stefan Manegold, and Manolis Koubarakis. 2014. GeoTriples: a Tool for Publishing Geospatial Data as RDF Graphs Using R2RML Mappings. In Proceedings of the ISWC 2014 Posters & Demonstrations Track a track within the 13th International Semantic Web Conference, Riva del Garda, Italy, October 21, 2014. 393--396. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Charalampos Nikolaou, Kallirroi Dogani, Konstantina Bereta, George Garbis, Manos Karpathiotakis, Kostis Kyzirakos, and Manolis Koubarakis. 2015. Sextant: Visualizing time-evolving linked geospatial data. Web Semantics: Science, Services and Agents on the World Wide Web , Vol. 35, 1 (2015). Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. George Papadakis, Konstantina Bereta, Themis Palpanas, and Manolis Koubarakis. 2017. Multi-core Meta-blocking for Big Linked Data. In Proceedings of the 13th International Conference on Semantic Systems, SEMANTICS 2017, Amsterdam, The Netherlands, September 11-14, 2017. 33--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Jorge Pérez, Marcelo Arenas, and Claudio Gutierrez. 2009. Semantics and Complexity of SPARQL. ACM Trans. Database Syst., Vol. 34, 3, Article 16 (Sept. 2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Matthew Perry and John Herring. 2012. GeoSPARQL - A geographic query language for RDF data. Open Geospatial Consortium (OGC) Implementation Standard. (2012).Google ScholarGoogle Scholar
  14. Mariano Rodriguez-Muro and Martin Rezk. 2015. Efficient SPARQL-to-SQL with R2RML mappings. Journal of Web Semantics , Vol. 33, 1 (2015).Google ScholarGoogle ScholarCross RefCross Ref
  15. Panayiotis Smeros and Manolis Koubarakis. 2016. Discovering Spatial and Temporal Links among RDF Data. In Proceedings of the Workshop on Linked Data on the Web, LDOW 2016, co-located with WWW.Google ScholarGoogle Scholar
  16. Wikipedia. 2018. Leaf Area Index - Wikipedia, The Free Encyclopedia. (2018). https://en.wikipedia.org/wiki/Leaf_area_indexGoogle ScholarGoogle Scholar

Index Terms

  1. From Copernicus Big Data to Big Information and Big Knowledge: A Demo from the Copernicus App Lab Project

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management
      October 2018
      2362 pages
      ISBN:9781450360142
      DOI:10.1145/3269206

      Copyright © 2018 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 17 October 2018

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CIKM '18 Paper Acceptance Rate147of826submissions,18%Overall Acceptance Rate1,861of8,427submissions,22%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader