Evaluating Alignment Approaches in Superimposed Time-Series and Temporal Event-Sequence Visualizations
| Autor: | Holly Jimison, Yixuan Zhang, Fangfang Sheng, Sara Di Bartolomeo, Cody Dunne |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Series (stratigraphy) Source code Event (computing) business.industry Computer science media_common.quotation_subject Computer Science - Human-Computer Interaction Static timing analysis 020207 software engineering 030209 endocrinology & metabolism Pattern recognition 02 engineering and technology Visualization Human-Computer Interaction (cs.HC) 03 medical and health sciences 0302 clinical medicine Data visualization Event sequence 0202 electrical engineering electronic engineering information engineering Task analysis Artificial intelligence business media_common |
| Zdroj: | IEEE VIS (Short Papers) |
| DOI: | 10.48550/arxiv.1908.07316 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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