Outlier quantification for multibeam data
| dc.contributor.author | Ziolkowski, Tobias | |
| dc.contributor.author | Koschmider, Agnes | |
| dc.contributor.author | Kröger, Peer | |
| dc.contributor.author | Devey, Colin | |
| dc.date.accessioned | 2022-09-09T12:40:17Z | |
| dc.date.available | 2022-09-09T12:40:17Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | This paper discusses the challenges of applying a data analytics pipeline for a large volume of data as can be found in natural and life sciences. To address this challenge, we attempt to elaborate an approach for an improved detection of outliers. We discuss an approach for outlier quantification for bathymetric data. As a use case, we selected ocean science (multibeam) data to calculate the outlierness for each data point. The benefit of outlier quantification is a more accurate estimation of which outliers should be removed or further analyzed. To shed light on the subject, this paper is structured as follows: first, a summary of related works on outlier detection is provided. The usefulness for a structured approach of outlier quantification is then discussed using multibeam data. This is followed by a presentation of the challenges for a suitable solution, and the paper concludes with a summary. | de |
| dc.identifier.doi | 10.1007/s00287-022-01469-w | |
| dc.identifier.pissn | 1432-122X | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s00287-022-01469-w | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39358 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Informatik Spektrum: Vol. 45, No. 4 | |
| dc.relation.ispartofseries | Informatik Spektrum | |
| dc.title | Outlier quantification for multibeam data | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 222 | |
| gi.citation.startPage | 218 |