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dc.contributor.authorSteiger, Martinen_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.authorSchader, Philippen_US
dc.contributor.authorKohlhammer, Jörnen_US
dc.contributor.editorE. Bertini and J. C. Robertsen_US
dc.date.accessioned2015-05-24T19:45:51Z
dc.date.available2015-05-24T19:45:51Z
dc.date.issued2015en_US
dc.identifier.urihttp://dx.doi.org/10.2312/eurova.20151105en_US
dc.description.abstractIn this paper, we present visual-interactive techniques for revealing relations between two co-existing multivariate feature spaces. Such data is generated, for example, by sensor networks characterized by a set of (categorical) attributes which continuously measure physical quantities over time. A challenging analysis task is the seeking for interesting relations between the time-oriented data and the sensor attributes. Our approach uses visualinteractive analysis to enable analysts to identify correlations between similar time series and similar attributes of the data. It is based on a combination of machine-based encoding of this information in position and color and the human ability to recognize cohesive structures and patterns. In our figures, we illustrate how analysts can identify similarities and anomalies between time series and categorical attributes of metering devices and sensors.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectH.5.2 [Information Interfaces and Presentation]en_US
dc.subjectUser Interfacesen_US
dc.subjectUseren_US
dc.subjectcentered designen_US
dc.titleVisual Analysis of Relations in Attributed Time-Series Dataen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)en_US
dc.description.sectionheadersTime-series and Temporal Dataen_US
dc.identifier.doi10.2312/eurova.20151105en_US
dc.identifier.pages61-65en_US


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