Knowledge-Driven Harmonization of Sensor Observations: Exploiting Linked Open Data for IoT Data Streams
Abstract:
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.
| Zugehörige Institution(en) am KIT | Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) |
| Publikationstyp | Hochschulschrift |
| Publikationsdatum | 02.06.2021 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-7315-1076-5 KITopen-ID: 1000128146 |
| Verlag | KIT Scientific Publishing |
| Umfang | VIII, 216 S. |
| Art der Arbeit | Dissertation |
| Fakultät | Fakultät für Wirtschaftswissenschaften (WIWI) |
| Institut | Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB) |
| Prüfungsdatum | 06.10.2020 |
| Schlagwörter | Internet der Dinge, Linked Open Data, Datenstromverarbeitung, Wissensgraph, Sensordatenharmonisierung, Internet of Things, Linked Open Data, data stream processing, corporate knowledge graph, sensor data harmonization |
| Referent/Betreuer | Sure-Vetter, Y. |