MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales
This addresses the lack of visual-interactive tools for multi-scale segmentation in biologging, primarily benefiting movement ecologists and similar domains, though it is incremental as it builds on existing segmentation techniques with new visualization methods.
The paper tackles the problem of segmenting biologging time series on multiple temporal scales by introducing MultiSegVA, a visual analytics platform that enables interactive definition of segmentation techniques and parameters, with expert feedback showing its effectiveness in enabling semantically meaningful analyses in movement ecology and other domains.
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.