Contributions to Proceedings:
C. Bors, C. Eichner, S. Miksch, C. Tominski, H. Schumann, T. Gschwandner:
"Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques";
in: "EuroVis 2020 - Short Papers",
issued by: Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta;
Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation
pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can
help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the
results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding
the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series
segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of
multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations,
while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement.
The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings
of general validity.
Visual Analytics, Time, Uncertainty
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.