A Visual Analytics Approach to Segmenting and Labeling Multivariate Time Series Data

Simple item page
dc.contributor.authorAlsallakh, Bilalen_US
dc.contributor.authorBögl, Markusen_US
dc.contributor.authorGschwandtner, Theresiaen_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.authorEsmael, Bilalen_US
dc.contributor.authorArnaout, Arghaden_US
dc.contributor.authorThonhauser, Gerharden_US
dc.contributor.authorZöllner, Philippen_US
dc.contributor.editorM. Pohl and J. Robertsen_US
dc.date.accessioned2014-12-16T07:19:33Z
dc.date.available2014-12-16T07:19:33Z
dc.date.issued2014en_US
dc.description.abstractMany natural and industrial processes such as oil well construction are composed of a sequence of recurring activities. Such processes can often be monitored via multiple sensors that record physical measurements over time. Using these measurements, it is sometimes possible to reconstruct the processes by segmenting the respective time series data into intervals that correspond to the constituent activities. While automated algorithms can compute this segmentation rapidly, they cannot always achieve the required accuracy rate e.g. due to process variations that need human judgment to account for. We propose a Visual Analytics approach that intertwines interactive time series visualization with automated algorithms for segmenting and labeling multivariate time series data. Our approach helps domain experts to inspect the results, identify segmentation problems, and correct mislabeled segments accordingly. We demonstrate how our approach is applied in the drilling industry and discuss its applicability to other domains having similar requirements.en_US
dc.description.seriesinformationEuroVis Workshop on Visual Analyticsen_US
dc.identifier.isbn978-3-905674-68-2en_US
dc.identifier.urihttps://doi.org/10.2312/eurova.20141142en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectG.3 [Probabilities and Statistics]en_US
dc.subjectTime series analysisen_US
dc.subjectI.5.2 [Pattern Recognition]en_US
dc.subjectDesign Methodologyen_US
dc.subjectClassifier design and evaluationen_US
dc.titleA Visual Analytics Approach to Segmenting and Labeling Multivariate Time Series Dataen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
031-035.pdf
Size:
478.77 KB
Format:
Adobe Portable Document Format
Download
Collections
EuroVA14