LEADER 01954nam  2200433z- 450 
001 9910493731503321
005 20231214133152.0
010   $a1000128146
035   $a(CKB)5590000000537507
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/71661
035   $a(EXLCZ)995590000000537507
100   $a20202108d2021      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aKnowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams
210   $aKarlsruhe$cKIT Scientific Publishing$d2021
215   $a1 electronic resource (236 p.)
311   $a3-7315-1076-6 
330   $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.
517   $aKnowledge-Driven Harmonization of Sensor Observations
606   $aEconomics$2bicssc
610   $aInternet der Dinge
610   $aLinked Open Data
610   $aDatenstromverarbeitung
610   $aWissensgraph
610   $aSensordatenharmonisierung
610   $aInternet of Things
610   $adata stream processing
610   $acorporate knowledge graph
610   $asensor data harmonization
615  7$aEconomics
700   $aFrank$b Matthias T$4auth$01331133
906   $aBOOK
912   $a9910493731503321
996   $aKnowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams$93040246
997   $aUNINA