Analyzing the skyline of NBA statistics using SkyLens: (a) a Projection View showing an overview of clusters and outliers; (b) a Tabular View depicting the attributes of four skyline players and reveals the factors making a player in skyline; (c) a Comparison View examining the differences between skyline players from the attribute and domination perspectives; (d) a Control Panel for refining skyline queries; (e) a pop-up window showing a detailed comparison between LeBron James and Chris Paul.

Description

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.

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