| dc.contributor.author | Koschmider, Agnes | |
| dc.contributor.author | Oppelt, Natascha | |
| dc.contributor.author | Hundsdörfer, Marie | |
| dc.date | 2022-08-01 | |
| dc.date.accessioned | 2022-09-09T12:40:19Z | |
| dc.date.available | 2022-09-09T12:40:19Z | |
| dc.date.issued | 2022 | |
| dc.identifier.issn | 1432-122X | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s00287-022-01470-3 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/39369 | |
| dc.description.abstract | The combination of machine learning techniques with process analytics like process mining might even significantly elevate novel insights into time series data collections. To efficiently analyze time series by process mining and to convey confidence into the analysis result, requires bridging challenges. The purpose of this article is to discuss these challenges and to present initial solutions. | de |
| dc.publisher | Springer | |
| dc.relation.ispartof | Informatik Spektrum: Vol. 45, No. 4 | |
| dc.relation.ispartofseries | Informatik Spektrum | |
| dc.title | Confidence-driven communication of process mining on time series | de |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 223-228 | |
| dc.identifier.doi | 10.1007/s00287-022-01470-3 | |
