Analyzing Semantically Enriched Trajectories
| dc.contributor.author | Seep, Jana | |
| dc.date | 2024-11-01 | |
| dc.date.accessioned | 2025-01-13T11:15:16Z | |
| dc.date.available | 2025-01-13T11:15:16Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | In order to understand what influences the movement of an object or person it is important to consider a variety of factors. These could be the visibility of certain landmarks, the current temperature or the presence of a crowded area to be avoided. These insights then can be used to understand movement in the public sector and improve our build environment, e.g. to reduce street traffic accidents or orientation in complex buildings. The following extended abstract is a summary of a doctoral thesis submitted to the University of Münster. The thesis was successfully defended in February 2023 [ 16 ]. The dissertation focuses on the analysis of so-called semantically enriched trajectories , which are used to describe observed movement. It proposes a new model based on an extended finite state machine, which allows for the representation and consideration of the information about the context of the trajectory. With the new model, we consider two main steps in trajectory analysis: First, we aim to infer a semantically enriched representative trajectory for a given cluster of trajectories. Second, we introduce a variation of the well-known k-means algorithm to calculate clusters based on the given context of trajectories. To show semantic feasibility of our approach, we conclude this work by evaluating the possibility to provide decision support for domain experts in two different public sector related contexts. | de |
| dc.identifier.doi | 10.1007/s13218-023-00818-5 | |
| dc.identifier.issn | 1610-1987 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13218-023-00818-5 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/45569 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 38, No. 3 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Clustering | |
| dc.subject | Semantic trajectories | |
| dc.subject | Similarity measure | |
| dc.subject | Trajectory analysis | |
| dc.subject | Wayfinding | |
| dc.title | Analyzing Semantically Enriched Trajectories | de |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 127-131 |