Improving access to big data in agriculture and forestry using semantic technologies

Rob Lokers*, Yke Van Randen, Rob Knapen, Stephan Gaubitzer, Sergey Zudin, Sander Janssen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

5 Citations (Scopus)

Abstract

To better understand and manage the interactions of agriculture and natural resources, for example under current increasing societal demands and climate changes, agro-environmental research must bring together an ever growing amount of data and information from multiple science domains. Data that is inherently large, multi-dimensional and heterogeneous, and requires computational intensive processing. Thus, agro-environmental researchers must deal with specific Big Data challenges in efficiently acquiring the data fit to their job while limiting the amount of computational, network and storage resources needed to practical levels. Automated procedures for collection, selection, annotation and indexing of data and metadata are indispensable in order to be able to effectively exploit the global network of available scientific information. This paper describes work performed in the EU FP7 Trees4Future and SemaGrow projects that contributes to development and evaluation of an infrastructure that allows efficient discovery and unified querying of agricultural and forestry resources using Linked Data and semantic technologies.

Original languageEnglish
Title of host publicationMetadata and Semantics Research
PublisherSpringer Verlag
Pages369-380
Volume544
ISBN (Print)9783319241289
DOIs
Publication statusPublished - 2015
Event9th Metadata and Semantics Research Conference, MTSR 2015 - Manchester, United Kingdom
Duration: 9 Sep 201511 Sep 2015

Publication series

NameCommunications in Computer and Information Science
Volume544
ISSN (Print)1865-0929

Conference

Conference9th Metadata and Semantics Research Conference, MTSR 2015
CountryUnited Kingdom
CityManchester
Period9/09/1511/09/15

Fingerprint

Forestry
Natural resources
Metadata
Climate change
Agriculture
Semantics
Processing
Big data

Keywords

  • Agriculture
  • Big data
  • Forestry
  • Metadata
  • Semantic technologies

Cite this

Lokers, R., Van Randen, Y., Knapen, R., Gaubitzer, S., Zudin, S., & Janssen, S. (2015). Improving access to big data in agriculture and forestry using semantic technologies. In Metadata and Semantics Research (Vol. 544, pp. 369-380). (Communications in Computer and Information Science; Vol. 544). Springer Verlag. https://doi.org/10.1007/978-3-319-24129-6_32
Lokers, Rob ; Van Randen, Yke ; Knapen, Rob ; Gaubitzer, Stephan ; Zudin, Sergey ; Janssen, Sander. / Improving access to big data in agriculture and forestry using semantic technologies. Metadata and Semantics Research. Vol. 544 Springer Verlag, 2015. pp. 369-380 (Communications in Computer and Information Science).
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Lokers, R, Van Randen, Y, Knapen, R, Gaubitzer, S, Zudin, S & Janssen, S 2015, Improving access to big data in agriculture and forestry using semantic technologies. in Metadata and Semantics Research. vol. 544, Communications in Computer and Information Science, vol. 544, Springer Verlag, pp. 369-380, 9th Metadata and Semantics Research Conference, MTSR 2015, Manchester, United Kingdom, 9/09/15. https://doi.org/10.1007/978-3-319-24129-6_32

Improving access to big data in agriculture and forestry using semantic technologies. / Lokers, Rob; Van Randen, Yke; Knapen, Rob; Gaubitzer, Stephan; Zudin, Sergey; Janssen, Sander.

Metadata and Semantics Research. Vol. 544 Springer Verlag, 2015. p. 369-380 (Communications in Computer and Information Science; Vol. 544).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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Lokers R, Van Randen Y, Knapen R, Gaubitzer S, Zudin S, Janssen S. Improving access to big data in agriculture and forestry using semantic technologies. In Metadata and Semantics Research. Vol. 544. Springer Verlag. 2015. p. 369-380. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-24129-6_32