Aufgrund von Vorbereitungen auf eine neue Version von KOPS, können am Montag, 6.2. und Dienstag, 7.2. keine Publikationen eingereicht werden. (Due to preparations for a new version of KOPS, no publications can be submitted on Monday, Feb. 6 and Tuesday, Feb. 7.)
Type of Publication: | Contribution to a conference collection |
Publication status: | Published |
URI (citable link): | http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1qnnk751tdpme5 |
Author: | Meschenmoser, Philipp; Buchmüller, Juri F.; Seebacher, Daniel; Wikelski, Martin; Keim, Daniel A. |
Year of publication: | 2020 |
Conference: | IEEE Conference on Visual Analytics Science and Technology (VAST) (Virtual Conference) 2020, Oct 25, 2020 - Oct 30, 2020 |
Published in: | Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST), 2020. - Piscataway, NJ : IEEE, 2020 |
ArXiv-ID: | arXiv:2009.00548 |
Summary: |
Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains.
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Subject (DDC): | 004 Computer Science |
Link to License: | In Copyright |
Bibliography of Konstanz: | Yes |
MESCHENMOSER, Philipp, Juri F. BUCHMÜLLER, Daniel SEEBACHER, Martin WIKELSKI, Daniel A. KEIM, 2020. MultiSegVA : Using Visual Analytics to Segment Biologging Time Series on Multiple Scales. IEEE Conference on Visual Analytics Science and Technology (VAST) (Virtual Conference) 2020, Oct 25, 2020 - Oct 30, 2020. In: Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST), 2020. Piscataway, NJ:IEEE
@inproceedings{Meschenmoser2020Multi-51034, title={MultiSegVA : Using Visual Analytics to Segment Biologging Time Series on Multiple Scales}, year={2020}, address={Piscataway, NJ}, publisher={IEEE}, booktitle={Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST), 2020}, author={Meschenmoser, Philipp and Buchmüller, Juri F. and Seebacher, Daniel and Wikelski, Martin and Keim, Daniel A.} }
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Meschenmoser_2-1qnnk751tdpme5.pdf | 160 |