This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.
The visual analysis of complex geo-spatial data is a challenging task. Typically, different views are used to communicate different aspects. With changing topics of interest, however, novel views are required. This leads to dynamically changing presentations of multiple views. This paper introduces a novel approach to support such scenarios. It allows for a spontaneous incorporation of views from different sources and to automatically layout these views in a multi-display environment. Furthermore, we introduce an enhanced undo/redo mechanism for this setting, which records user interactions and, in this way, enables swift reconfigurations of displayed views. Hence, users can fluently switch the focus of visual analysis without extensive manual interactions. We demonstrate our approach by the particular use case of discussing geo-spatial climate data.
There are many expressive visualization techniques for analyzing graphs. Yet, there is only little research on how existing visual representations can be employed to support data editing. An increasingly relevant task when working with graphs is the editing of node attributes. We propose an integrated visualize-and-edit approach to editing attribute values via direct interaction with the visual representation. The visualize part is based on node-link diagrams paired with attribute-dependent layouts. The edit part is as easy as moving nodes via drag-and-drop gestures. We present dedicated interaction techniques for editing quantitative as well as qualitative attribute data values. The benefit of our novel integrated approach is that one can directly edit the data while the visualization constantly provides feedback on the implications of the data modifications. Preliminary user feedback indicates that our integrated approach can be a useful complement to standard non-visual editing via external tools.
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