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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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9783731510765
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Taschenbuch. Zustand: Neu. Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams | Matthias T. Frank | Taschenbuch | Paperback | Englisch | 2021 | Karlsruher Institut für Technologie | EAN 9783731510765 | Verantwortliche Person für die EU: KIT Scientific Publishing, Straße am Forum 2, 76131 Karlsruhe, info[at]ksp[dot]kit[dot]edu | Anbieter: preigu. Artikel-Nr. 120350447
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