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Title: Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations
Composite temporal event sequence visualizations have included sentinel event alignment techniques to cope with data volume and variety. Prior work has demonstrated the utility of using single-event alignment for understanding the precursor, co-occurring, and aftereffect events surrounding a sentinel event. However, the usefulness of single-event alignment has not been sufficiently evaluated in composite visualizations. Furthermore, recently proposed dual-event alignment techniques have not been empirically evaluated. In this work, we designed tasks around temporal event sequence and timing analysis and conducted a controlled experiment on Amazon Mechanical Turk to examine four sentinel event alignment approaches: no sentinel event alignment (NoAlign), single-event alignment (SingleAlign), dual-event alignment with left justification (DualLeft), and dual-event alignment with stretch justification (DualStretch). Differences between approaches were most pronounced with more rows of data. For understanding intermediate events between two sentinel events, dual-event alignment was the clear winner for correctness-71% vs. 18% for NoAlign and SingleAlign. For understanding the duration between two sentinel events, NoAlign was the clear winner: correctness-88% vs. 36% for DualStretch- completion time-55 seconds vs. 101 seconds for DualLeft-and error-1.5% vs. 8.4% for DualStretch. For understanding precursor and aftereffect events, there was no significant difference among approaches. A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/78fs5  more » « less
Award ID(s):
1755901
PAR ID:
10157013
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2019 IEEE Visualization Conference (VIS)
Page Range / eLocation ID:
1 - 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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