  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.
