Evaluating alignment approaches in superimposed time-series and temporal event-sequence visualizations

Comparison of the different alignment methods taken into account in the paper: No Alignment, Single-event Alignment, Dual-event Alignment with left justification and Dual-event Alignment with stretch justification.
User task performance was evaluated on four temporal event sequence alignment visualization 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). Stimuli were created using IDMVis [15] and Type 1 diabetes treatment data. Measures were task completion time, correctness, and error rate.
Abstract
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.
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Khoury Vis Lab — Northeastern University
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