Anomaliedetektion in räumlich-zeitlichen Datensätzen
Anthology / Conference Proceedings (Sammelband / Tagungsbände)
| dc.contributor.author | Anneken, Mathias | |
| dc.date.accessioned | 2023-08-29T07:29:03Z | |
| dc.date.available | 2023-08-29T07:29:03Z | |
| dc.date.issued | 2023 | |
| dc.identifier | OCN: 1403109722 | |
| dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/75885 | |
| dc.description.abstract | Human support in surveillance tasks is crucial due to the overwhelming amount of sensor data. This work focuses on the development of data fusion methods using the maritime domain as an example. Various anomalies are investigated, evaluated using real vessel traffic data and tested with experts. For this purpose, situations of interest and anomalies are modelled and evaluated based on different machine learning methods. | en_US |
| dc.language | German | en_US |
| dc.relation.ispartofseries | Karlsruher Schriften zur Anthropomatik | en_US |
| dc.subject.other | spatio-temporal data; situation analysis; anomaly detection; räumlich-zeitliche Daten; Maritime Überwachung; Anomaliedetektion; maritime surveillance; Situationsanalyse; machine learning; Maschinelles Lernen | en_US |
| dc.title | Anomaliedetektion in räumlich-zeitlichen Datensätzen | en_US |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000158519 | en_US |
| oapen.relation.isPublishedBy | 44e29711-8d53-496b-85cc-3d10c9469be9 | en_US |
| oapen.collection | AG Universitätsverlage | |
| oapen.series.number | 51 | en_US |
| oapen.pages | 264 | en_US |
| peerreview.anonymity | Single-anonymised | |
| peerreview.id | 2e56347d-034c-4741-9c0e-93c383a81b66 | |
| peerreview.open.review | No | |
| peerreview.publish.responsibility | Publisher | |
| peerreview.review.stage | Pre-publication | |
| peerreview.review.type | Full text | |
| peerreview.reviewer.type | Editorial board member | |
| peerreview.title | Anthology / Conference Proceedings (Sammelband / Tagungsbände) |
