000			01980naaaa2200433uu 4500
003			BUT
005			20230330104247.0
006			m o d
007			cr|mn|---annan
008			20210816s2021 xx |||||o ||| eng|| d
020			_aKSP/1000128146
020			_a9783731510765
040			_aoapen _coapen
041	0		_aeng
080			_a004
100	1		_aFrank, Matthias T. _4auth
245	1	0	_aKnowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams
260			_aKarlsruhe _bKIT Scientific Publishing _c2021
300			_a1 electronic resource (236 p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aThe 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.
540			_aCreative Commons _fby-sa/4.0 _2cc
546			_aEnglish
650		0	_aЭлектронный бизнес _94523
650		0	_aБазы данных _92229
653			_aInternet der Dinge
653			_aLinked Open Data
653			_aDatenstromverarbeitung
653			_aWissensgraph
653			_aSensordatenharmonisierung
653			_aInternet of Things
653			_adata stream processing
653			_acorporate knowledge graph
653			_asensor data harmonization
856	4	0	_awww.oapen.org _uhttps://library.oapen.org/bitstream/id/d4f74649-aed1-460f-99e7-d6724191b9e3/9783731510765.pdf _70 _zDownload
856	4	0	_awww.oapen.org _uhttps://library.oapen.org/handle/20.500.12657/50449 _70 _zDescription
909			_c4 _dDarya Shvetsova
942			_2udc _cEE
999			_c6046 _d6046