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dc.contributor.authorFrank, Matthias T.
dc.date.accessioned2021-08-16T09:31:21Z
dc.date.available2021-08-16T09:31:21Z
dc.date.issued2021
dc.identifierONIX_20210816_9783731510765_10
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/50449
dc.description.abstractThe 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.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::K Economics, finance, business & management::KC Economics
dc.subject.otherInternet der Dinge
dc.subject.otherLinked Open Data
dc.subject.otherDatenstromverarbeitung
dc.subject.otherWissensgraph
dc.subject.otherSensordatenharmonisierung
dc.subject.otherInternet of Things
dc.subject.otherdata stream processing
dc.subject.othercorporate knowledge graph
dc.subject.othersensor data harmonization
dc.titleKnowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams
dc.typebook
oapen.identifier.doi10.5445/KSP/1000128146
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9
oapen.relation.isbn9783731510765
oapen.imprintKIT Scientific Publishing
oapen.pages236
oapen.place.publicationKarlsruhe


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