  Visual data analysis tools provide people with the agency and flexibility to
explore data using a variety of interactive functionalities. However, this
flexibility may introduce potential consequences in situations where users
unknowingly overemphasize or underemphasize specific subsets of the data or
attribute space they are analyzing. For example, users may overemphasize
specific attributes and/or their values (e.g., Gender is always encoded on the
X axis), underemphasize others (e.g., Religion is never encoded), ignore a
subset of the data (e.g., older people are filtered out), etc. In response, we
present Lumos, a visual data analysis tool that captures and shows the
interaction history with data to increase awareness of such analytic behaviors.
Using in-situ (at the place of interaction) and ex-situ (in an external view)
visualization techniques, Lumos provides real-time feedback to users for them
to reflect on their activities. For example, Lumos highlights datapoints that
have been previously examined in the same visualization (in-situ) and also
overlays them on the underlying data distribution (i.e., baseline distribution)
in a separate visualization (ex-situ). Through a user study with 24
participants, we investigate how Lumos helps users' data exploration and
decision-making processes. We found that Lumos increases users' awareness of
visual data analysis practices in real-time, promoting reflection upon and
acknowledgement of their intentions and potentially influencing subsequent
interactions.
