论文标题
rankaxis：在多属性数据探索中进行投影和排名的系统组合
RankAxis: Towards a Systematic Combination of Projection and Ranking in Multi-Attribute Data Exploration
论文作者
论文摘要
投影和排名是多属性数据探索中经常使用的分析技术。两个技术家族都可以帮助分析师完成任务，例如识别观测值和确定有序子组之间的相似性，并在多属性数据探索中表现出良好的性能。但是，它们经常表现出诸如扭曲的投影布局，晦涩的语义解释以及通过选择（加权）属性子集产生的非直觉效应等问题。此外，很少有研究试图将投影并将其排名为相同的探索空间，以补充彼此的优势和劣势。因此，我们提出了rankaxis，这是一种视觉分析系统，该系统系统地结合了投影和排名，以促进这两种技术的相互解释，并共同支持多属性数据探索。现实世界中的案例研究，专家反馈和用户研究证明了rankaxis的功效。
Projection and ranking are frequently used analysis techniques in multi-attribute data exploration. Both families of techniques help analysts with tasks such as identifying similarities between observations and determining ordered subgroups, and have shown good performances in multi-attribute data exploration. However, they often exhibit problems such as distorted projection layouts, obscure semantic interpretations, and non-intuitive effects produced by selecting a subset of (weighted) attributes. Moreover, few studies have attempted to combine projection and ranking into the same exploration space to complement each other's strengths and weaknesses. For this reason, we propose RankAxis, a visual analytics system that systematically combines projection and ranking to facilitate the mutual interpretation of these two techniques and jointly support multi-attribute data exploration. A real-world case study, expert feedback, and a user study demonstrate the efficacy of RankAxis.
