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Enhancing time series segmentation and labeling through the knowledge generation model

Poster presented at the Eurographics Conference on Visualization, EuroVis 2015, 25-29 May 2015, Cagliari, Sardinia, Italy
: Gschwandtner, Theresia; Schumann, Heidrun; Bernard, Jürgen; May, Thorsten; Bögl, Markus; Miksch, Silvia; Kohlhammer, Jörn; Röhlig, Martin; Alsallakh, Bilal

Poster urn:nbn:de:0011-n-4615066 (502 KByte PDF)
MD5 Fingerprint: 8ade565ccb3c80860e440c2cd7609a51
Created on: 19.8.2017

Fulltext urn:nbn:de:0011-n-461506-17 (190 KByte PDF)
MD5 Fingerprint: 9e012ff4525d4ab3a03fbf2f3822ee30
Created on: 19.8.2017

2015, 1 Folie
Eurographics Conference on Visualization (EuroVis) <17, 2015, Cagliari>
Poster, Electronic Publication
Fraunhofer IGD ()
time series analysis; Multivariate data; visual analytic; Business Field: Visual decision support; Research Area: Modeling (MOD)

Segmentation and labeling of different activities in multivariate time series data is an important task in many domains. There is a multitude of automatic segmentation and labeling methods available, which are designed to handle different situations. These methods can be used with multiple parametrizations, which leads to an overwhelming amount of options to choose from. To this end, we present a conceptual design of a Visual Analytics framework (1) to select appropriate segmentation and labeling methods with appropriate parametrizations, (2) to analyze the (multiple) results, (3) to understand different kinds and origins of uncertainties in these results, and (4) to reason which methods and which parametrizations yield stable results and fine-tune these configurations if necessary.