Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model

TitleEnhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model
Publication TypeConference Paper
Year of Publication2015
AuthorsGschwandtner, T., H. Schuman, J. Bernard, T. May, M. Bögl, S. Miksch, J. Kohlhammer, M. Röhlig, and B. Alsallakh
EditorsMaciejewski, R., and F. Marton
Conference Name Poster Proceedings of the Eurographics Conference on Visualization (EuroVis 2015)
Pages3
PublisherThe Eurographics Association
Conference LocationCagliari, Italy
KeywordsLabeling, Segmentation, time series, Visual analytics
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.

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paper203.68 KB
poster516.58 KB
Funding projects: 
CVAST
Funding projects: 
VISSECT