@inproceedings{Meschenmoser2020Multi-51034,
  year={2020},
  title={MultiSegVA : Using Visual Analytics to Segment Biologging Time Series on Multiple Scales},
  publisher={IEEE},
  address={Piscataway, NJ},
  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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    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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