Abstract

Decision makers often find themselves in situations where they need to consider time-varying values for multi-criteria decision-making. Skyline queries are one of the most widely used methods of approaching multi-criteria decision-making problems because they reduce the size of search space by excluding inferior data. However, skylines in time-series data fluctuate with changes in attributes. Moreover, the number of skyline points increases as the number of dimensions increases, and the skyline query itself does not provide any ranking method. Thus, users are required to direct a considerable amount of effort into analyzing and finding the best selection. To address these issues, we propose SkyFlow, a visual analytical system for comparing time-varying data to facilitate the decision-making process. We apply two datasets in our system and describe scenarios to demonstrate the effectiveness of SkyFlow. In addition, we conduct a qualitative study to highlight the efficiency of our system in assisting users to compare candidates and make decisions involving time-series data.

Graphic abstract

中文翻译：

---

SkyFlow：按时间序列对高维天际线的可视化分析

摘要

决策者经常发现自己需要考虑随时间变化的值以进行多准则决策。天际线查询是解决多准则决策问题的最广泛使用的方法之一，因为它们通过排除劣等数据来减小搜索空间的大小。但是，时间序列数据中的天际线会随着属性的变化而波动。此外，天际线点的数量随维数的增加而增加，并且天际线查询本身不提供任何排名方法。因此，要求用户花费大量的精力来分析和找到最佳选择。为了解决这些问题，我们提出了SkyFlow，这是一种可视分析系统，用于比较时变数据以促进决策过程。我们在系统中应用了两个数据集，并描述了一些场景以演示SkyFlow的有效性。此外，我们进行了定性研究，以突出我们系统在协助用户比较候选人和做出涉及时序数据的决策方面的效率。

图形摘要