论文标题
MultiseGVA:使用视觉分析来分段多个尺度上的生物学时间序列
MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales
论文作者
论文摘要
在多个时间尺度上将动物的生物学时间序列分割是一个必不可少的步骤,它需要具有仔细参数化和可能跨域专业知识的复杂技术。然而,缺乏强烈支持这种多尺度分割的视觉相互作用工具。为了缩小这一差距,我们介绍了我们的多式va平台,用于在多个时间尺度上交互定义分割技术和参数。 MultiseGVA主要贡献量身定制的视觉相互作用手段和视觉分析范例,用于在多个尺度上分割未标记的时间序列。此外,为了灵活地构成多尺度细分,该平台贡献了一种新的视觉查询语言,该语言将各种细分技术链接起来。为了说明我们的方法,我们提出了与运动生态学家合作得出的面向域的分割技术集。我们证明了来自运动生态学的两个现实世界用例中多ESESGVA的适用性和实用性,与环境感知的分割后以及渐进式聚类相关的行为分析。运动生态学家的专家反馈显示了量身定制的视觉相互作用手段和视觉分析范式在分割多尺度数据方面的有效性,从而使它们能够进行语义有意义的分析。第三个用例表明,多Esegva可以推广到其他域。
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.