Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data

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dc.contributor.authorBögl, Markusen_US
dc.contributor.authorBors, Christianen_US
dc.contributor.authorGschwandtner, Theresiaen_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.editorAnna Puig and Renata Raidouen_US
dc.date.accessioned2018-06-02T17:55:51Z
dc.date.available2018-06-02T17:55:51Z
dc.date.issued2018
dc.description.abstractThe segmenting and labeling of multivariate time series data is applied in different domains, e.g. activity recognition or sensor states. This involves several steps of (pre-) processing, segmenting, and labeling of time intervals, and visually exploring the results as well as iteratively refining the parameters for all the processing steps. Within these processes different uncertainties are involved and relevant. In this poster we identify and categorize important uncertainties in this problem domain. We discuss challenges for visually communicating these uncertainties throughout the segmenting and labeling process.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2018 - Posters
dc.identifier.doi10.2312/eurp.20181126
dc.identifier.isbn978-3-03868-065-9
dc.identifier.pages45-47
dc.identifier.urihttps://doi.org/10.2312/eurp.20181126
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurp20181126
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization theory
dc.subjectconcepts and paradigms
dc.subjectMathematics of computing
dc.subjectTime series analysis
dc.titleCategorizing Uncertainties in the Process of Segmenting and Labeling Time Series Dataen_US
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