@conference{426,
  keywords = {Visual analytics, time series, Segmentation, Labeling},
  author = {Theresia Gschwandtner and Heidrun Schumann and Jürgen Bernard and Thorsten May and Markus Bögl and Silvia Miksch and Jörn Kohlhammer and Martin Röhlig and Bilal Alsallakh},
  title = {Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model},
  abstract = {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.
},
  year = {2015},
  journal = { Poster Proceedings of the Eurographics Conference on Visualization (EuroVis 2015)},
  pages = {3},
  publisher = {The Eurographics Association},
  address = {Cagliari, Italy},
}