Talks and Poster Presentations (with Proceedings-Entry):

T. Gschwandtner, H. Schumann, J. Bernard, T. May, M. Bögl, S. Miksch, J. Kohlhammer, M. Röhlig, B. Alsallakh:
"Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model";
Poster: Eurographics Conference on Visualization (EuroVis 2015), Cagliari, Sardinia, Italy; 2015-05-25 - 2015-05-29; in: "Proceedings of the Eurographics Conference on Visualization (EuroVis) - Posters 2015", R. Maciejewski, F. Marton (ed.); Eurographics Association, (2015), Paper ID 11, 3 pages.

English 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.

time series, segmentation, labeling, Visual Analytics

Electronic version of the publication:

Related Projects:
Project Head Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)

Created from the Publication Database of the Vienna University of Technology.