01971nam 2200445z- 450 9910493731503321202108171000128146(CKB)5590000000537507(oapen)https://directory.doabooks.org/handle/20.500.12854/71661(oapen)doab71661(EXLCZ)99559000000053750720202108d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierKnowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data StreamsKarlsruheKIT Scientific Publishing20211 online resource (236 p.)3-7315-1076-6 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.Knowledge-Driven Harmonization of Sensor ObservationsEconomicsbicssccorporate knowledge graphdata stream processingDatenstromverarbeitungInternet der DingeInternet of ThingsLinked Open Datasensor data harmonizationSensordatenharmonisierungWissensgraphEconomicsFrank Matthias Tauth1331133BOOK9910493731503321Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams3040246UNINA