Characterizing the Quality of Insight by Interactions: A Case Study

Chen He, Luana Micallef, Liye He, Gopal Peddinti, Tero Aittokallio, Giulio Jacucci

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This paper presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.

Original languageEnglish
Number of pages13
JournalIEEE Transactions on Visualization and Computer Graphics
DOIs
Publication statusAccepted/In press - 2 Mar 2020
MoE publication typeA1 Journal article-refereed

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