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Composite temporal event sequence visualizations have included sentinel eventalignment techniques to cope with data volume and variety. Prior work hasdemonstrated the utility of using single-event alignment for understanding theprecursor, co-occurring, and aftereffect events surrounding a sentinel event.However, the usefulness of single-event alignment has not been sufficientlyevaluated in composite visualizations. Furthermore, recently proposeddual-event alignment techniques have not been empirically evaluated. In thiswork, we designed tasks around temporal event sequence and timing analysis andconducted a controlled experiment on Amazon Mechanical Turk to examine foursentinel event alignment approaches: no sentinel event alignment (NoAlign),single-event alignment (SingleAlign), dual-event alignment with leftjustification (DualLeft), and dual-event alignment with stretch justification(DualStretch). Differences between approaches were most pronounced with morerows of data. For understanding intermediate events between two sentinelevents, dual-event alignment was the clear winner for correctness---71% vs. 18%for NoAlign and SingleAlign. For understanding the duration between twosentinel events, NoAlign was the clear winner: correctness---88% vs. 36% forDualStretch---completion time---55 seconds vs. 101 seconds for DualLeft---anderror---1.5% vs. 8.4% for DualStretch. For understanding precursor andaftereffect events, there was no significant difference among approaches. Afree copy of this paper, the evaluation stimuli and data, and source code areavailable at https://osf.io/78fs5

Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations