  Skyline queries have wide-ranging applications in fields that involve
multi-criteria decision making, including tourism, retail industry, and human
resources. By automatically removing incompetent candidates, skyline queries
allow users to focus on a subset of superior data items (i.e., the skyline),
thus reducing the decision-making overhead. However, users are still required
to interpret and compare these superior items manually before making a
successful choice. This task is challenging because of two issues. First,
people usually have fuzzy, unstable, and inconsistent preferences when
presented with multiple candidates. Second, skyline queries do not reveal the
reasons for the superiority of certain skyline points in a multi-dimensional
space. To address these issues, we propose SkyLens, a visual analytic system
aiming at revealing the superiority of skyline points from different
perspectives and at different scales to aid users in their decision making. Two
scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of
attributes. A qualitative study is also conducted to show that users can
efficiently accomplish skyline understanding and comparison tasks with SkyLens.
