Visual Analytics Interface for Time Series Data based on Trajectory Manipulation

Dec 9, 20181 like646 views

This document presents a visual analytics interface for time series data based on trajectory manipulation. The interface allows users to explicitly control playback period and speed of animation to show temporal changes in attribute values. It also allows grouping of trajectories by sketch-based input or clicking on trajectories. Evaluation results showed that animation traces were effective for trend visualization and that interaction frequencies tended to be smaller for description tasks than visualization generation tasks.

Visual Analytics Interface for Time Series Data
based on Trajectory Manipulation
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(a) Scatter plot view (b) Detailed view
A convex hull: grouping objects
by sketch-based input
(in Pause mode)
A convex hull: grouping of
trajectories by trajectory click
A trajectory
Animation control UI
Show temporal changes
of attribute values
explicitly control playback period/speed of animation
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G. Robertson, R. Fernandez, D. Fisher, B. Lee, J. Stasko, Effectiveness of animation in
trend visualization, IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1325-1332, 2008.
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B. Kondo, C. Collins, DimpVis: Exploring Time-varying Information Visualizations by
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data - a systematic view, Computers & Graphics, Vol. 31, No. 3, pp. 401–409, 2007.
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Interaction
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• Description task: tended to be smaller
than that in the visualization
generation task
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8. (a) Scatter plot view (b) Detailed view A convex hull: grouping objects by sketch-based input (in Pause mode) A convex hull: grouping of trajectories by trajectory click A trajectory Animation control UI Show temporal changes of attribute values explicitly control playback period/speed of animation 8/20

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13. 0 500 1000 1500 2000 2500 3000 PL BL1 BL2 PL BL1 BL2 PL BL2 PL BL2 Interactionfrequency Desc1 Desc2 Desc3 Desc4 Desc5 Vis1 Vis2 Vis3 Vis4 Vis5 Vis6 Vis7 Vis8 Vis9 F1 F2 F3 F4 • • • • • • 13/20

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17. G. Robertson, R. Fernandez, D. Fisher, B. Lee, J. Stasko, Effectiveness of animation in trend visualization, IEEE Transactions on Visualization and Computer Graphics, Vol. 14, No. 6, pp. 1325-1332, 2008. S. Kriglstein, M. Pohl, C. Stachl, Animation for time-oriented data: An overview of empirical research, In 2012 16th International Conference on Information Visualization (IV ’12), pp. 30-35, 2012. A. Endert, P. Fiaux, C. North , Semantic Interaction for Visual Text Analytics, Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 473-482, 2012. B. Bach, C. Shi, N. Heulot, T. Madhyastha, T. Grabowski, P. Dragicevic, Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data, IEEE Transactions on Visualization and Computer Graphics, Vol. 22, No. 1, pp. 559-568, 2016. D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, H. Ziegler,Visual Analytics: Scope and Challenges, in S. J. Simoff, M. H. Böhlen, A. Mazeika, eds, Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, pp. 76–90, Springer, 2008. 17/20

18. B. Kondo, C. Collins, DimpVis: Exploring Time-varying Information Visualizations by Direct Manipulation, IEEE Transactions on Visualization and Computer Graphics, Vol. 20, No. 12, pp. 2003-2012, 2014. W. Aigner, S. Miksch, W. Müller, H. Schumann, C. Tominski, Visualizing time-oriented data - a systematic view, Computers & Graphics, Vol. 31, No. 3, pp. 401–409, 2007. 18/20

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22. • • 0 100 200 300 400 Vis-1 Vis-2 Vis-3 Vis-4 Vis-5 Vis-6 Vis-7 Vis-8 Vis-9 animation traces proposed 22/20

23. • • • • • 0 50 100 150 200 250 300 PL BL2 PL BL2 PL PL PL BL1 InteractionFrequency Desc1 Desc2 Desc3 Desc4 Desc5 Vis1 Vis2 Vis3 Vis4 Vis5 Vis6 Vis7 Vis8 Vis9 F5 F6 F7 F8 F9 • Description task: tended to be smaller than that in the visualization generation task 23/20

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