Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model : Poster presented at the Eurographics Conference on Visualization (EuroVis).
Eurographics Conference on Visualization, EuroVis 2015. (25.05.2015-29.05.2015)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Art des Eintrags: | Bibliographie |
| Titel: | Enhancing Time Series Segmentation and Labeling Through the Knowledge Generation Model : Poster presented at the Eurographics Conference on Visualization (EuroVis) |
| Sprache: | Englisch |
| Publikationsjahr: | Mai 2015 |
| Ort: | Cagliari, Sardinia, Italy |
| Veranstaltungstitel: | Eurographics Conference on Visualization, EuroVis 2015 |
| Veranstaltungsdatum: | 25.05.2015-29.05.2015 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Modeling (MOD), Time series analysis, Multivariate data, Visual analytics |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 21 Jun 2019 08:06 |
| Letzte Änderung: | 21 Jun 2019 08:06 |
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