SkyLens: Visual Analysis of Skyline on Multi-dimensional Data
This addresses the problem of manual interpretation and comparison of skyline points for users in fields like tourism and retail, though it is incremental as it builds on existing skyline query methods.
The authors tackled the challenge of interpreting and comparing skyline query results in multi-dimensional data by developing SkyLens, a visual analytic system that reveals the superiority of skyline points from different perspectives and scales, with a qualitative study showing users can efficiently accomplish skyline understanding and comparison tasks.
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.