Talks and Poster Presentations (with Proceedings-Entry):
M. Röhlig, M. Luboschik, H. Schumann, M. Bögl, B. Alsallakh, S. Miksch:
"Analyzing Parameter Influence on Time-Series Segmentation and Labeling";
Poster: Ieee Vis 2014,
- 2014-11-14; in: "Poster Proceedings of the IEEE Visualization Conference 2014",
G. Andrienko, E. Bertini, H. Carr, N. Elmqvist, B. Lee, H. Leitte (ed.);
Reconstructing processes from measurements of multiple sensors over time is an important task in many application domains. For the reconstruction, these multivariate time-series can be automatically processed. However, the outcomes of automated algorithms often vary in quality and show strong parameter dependencies, making manual inspections and adjustments of the results necessary. We propose a visual analysis approach to support the user in understanding parameters' influences on these results. With our approach the user can identify and select parameter settings that meet certain quality criteria. The proposed visual and interactive design helps to identify relationships and temporal patterns, supports subsequent decision making, and promotes higher accuracy as well as confidence in the results.
Visual Analytics, Segmentation, Labeling, Multivariate Time Series
Electronic version of the publication:
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.