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

  author    = {Zhang, Yixuan and Di~Bartolomeo, Sara and Sheng, Fangfang and Dunne, Cody},
  booktitle = {Proc. IEEE Visualization Conference},
  title     = {Evaluating alignment approaches in superimposed time-series and temporal event-sequence visualizations},
  year      = {2019},
  note      = {Preprint \& supplemental material: https://osf.io/q764s},
  number    = {1},
  pages     = {512-522},
  series    = {VIS},
  volume    = {25},
  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 https://osf.io/78fs5},
  doi       = {10.1109/VISUAL.2019.8933584},
  issn      = {1077-2626},

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