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

TL;DR

This study evaluates different sentinel event alignment techniques in composite temporal visualizations through controlled experiments, revealing their strengths and limitations for various analysis tasks.

Contribution

It provides an empirical comparison of four sentinel event alignment approaches, including two novel dual-event methods, in the context of time-series and event-sequence visualization tasks.

Findings

Dual-event alignment improves correctness for intermediate event understanding.

NoAlign is most effective for duration estimation between sentinel events.

Approach differences are more significant with larger data sets.

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