Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams
(Sprache: Englisch)
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....
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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.
Bibliographische Angaben
- Autor: Matthias Frank
- 2021, 236 Seiten, mit Abbildungen, Maße: 17 x 24 cm, Kartoniert (TB), Englisch
- Verlag: KIT Scientific Publishing
- ISBN-10: 3731510766
- ISBN-13: 9783731510765
Sprache:
Englisch
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