论文标题
COHORTVA：基于历史数据的互动探索互动探索的视觉分析系统
CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data
论文作者
论文摘要
在历史研究中，人群分析试图通过研究历史人物的基于群体的行为来识别社会结构和人物的迁移率。先前的工作主要采用自动数据挖掘方法，缺乏有效的视觉解释。在本文中，我们提出了Cohortva，这是一种交互式视觉分析方法，使历史学家能够将专业知识和洞察力纳入迭代探索过程。 COHORTVA的内核是一种新颖的识别模型，它通过由大规模历史数据库构建的预构建的知识图生成候选人群并构建了同类特征。我们提出了一组协调的观点，以说明已识别的人群和特征以及历史事件和人物概况。两项案例研究和与历史学家的访谈表明，同龄人可以大大提高队列识别，人物身份验证和假设产生的能力。
In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.
