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The rise of the Internet of Things leads to an unprecedented number of continuous sensor observations that are available as IoT data streams. Harmonization of such observations is a labor-intensive task due to heterogeneity in format, syntax, and semantics. We aim to reduce the effort for such harmonization tasks by employing a knowledge-driven approach. To this end, we pursue the idea of exploiting the large body of formalized public knowledge represented as statements in Linked Open Data.

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DETAILS

  • Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams
  • Frank, Matthias
  • Kartoniert, 236 S.
  • graph. Darst.
  • Sprache: Englisch
  • 24 cm
  • ISBN-13: 978-3-7315-1076-5
  • Titelnr.: 92078480
  • Gewicht: 575 g
  • KIT Scientific Publishing (2021)
  • Herstelleradresse

    KIT Scientific Publishing

    Strasse am Forum 2

    76131 - DE Karlsruhe

    E-Mail: info@ksp.kit.edu

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