Visual analytics of set data for knowledge discovery and member selection support
Visual analytics of set data for knowledge discovery and member selection support
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DOI：
10.1016/j.dss.2021.113635
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Ryuji Watanabe;Hideaki Ishibashi;T. Furukawa
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文献类型：
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作者：
Ryuji Watanabe;Hideaki Ishibashi;T. Furukawa
可视化分析（VA）是一种可视化辅助的探索性分析方法，其中以人为中心的方式在用户和系统之间交互地执行知识发现。本研究的目的是开发一种方法，VA的设置数据，旨在支持知识发现和成员选择。一个典型的目标应用程序是团队分析和成员选择的可视化支持系统，用户可以通过它分析过去的团队并检查新团队的候选阵容。由于存在组合爆炸问题等困难，开发集数据的VA系统具有挑战性。在这项研究中，我们首先定义了目标系统应满足的要求，并澄清了随之而来的挑战。然后提出了一种满足要求的集合数据VA方法。其核心思想是使用流形网络模型来模拟集合及其输出的生成过程。所提出的方法可视化的相关因素作为一组地形图上的各种信息可视化。此外，使用地形图作为双向接口，用户可以在这些地图上指示他们在系统中感兴趣的目标。我们证明了所提出的方法，将其应用到篮球队，并与基准系统的结果预测和阵容重建任务进行比较。由于该方法可以通过扩展网络结构来适应具体的应用情况，因此它可以成为一种通用的方法，可以用来建立实际的系统。
Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered manner. The purpose of this study is to develop a method for the VA of set data aimed at supporting knowledge discovery and member selection. A typical target application is a visual support system for team analysis and member selection, by which users can analyze past teams and examine candidate lineups for new teams. Because there are several difficulties, such as the combinatorial explosion problem, developing a VA system of set data is challenging. In this study, we first define the requirements that the target system should satisfy and clarify the accompanying challenges. Then we propose a method for the VA of set data, which satisfies the requirements. The key idea is to model the generation process of sets and their outputs using a manifold network model. The proposed method visualizes the relevant factors as a set of topographic maps on which various information is visualized. Furthermore, using the topographic maps as a bidirectional interface, users can indicate their targets of interest in the system on these maps. We demonstrate the proposed method by applying it to basketball teams, and compare with a benchmark system for outcome prediction and lineup reconstruction tasks. Because the method can be adapted to individual application cases by extending the network structure, it can be a general method by which practical systems can be built.