期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 28, 期 9, 页码 3070-3081出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2021.3050497
关键词
Image color analysis; Biology; Visualization; Shape; Task analysis; Pattern analysis; Proteins; Event sequence visualization; sequence alignment; evaluation; user study
资金
- Alexander von Humboldt Foundation
- Engineering and Physical Sciences Research Council [EP/N013980/1]
- Alan Turing Institute Fellowship
This article investigates the impact of local and global alignment techniques on people's performance in judging sequence similarity. The experiment showed that the global alignment group had higher accuracy and participants' response times were influenced by the number of event types, sequence similarity, and edit types.
Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This article describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic versus local versus global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98 versus 93 percent correct), with the basic group getting 95 percent correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.
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