| Autor: |
Zhang, Wei, Wong, Jason K., Wang, Xumeng, Gong, Youcheng, Zhu, Rongchen, Liu, Kai, Yan, Zihan, Tan, Siwei, Qu, Huamin, Chen, Siming, Chen, Wei |
| Zdroj: |
IEEE Transactions on Visualization and Computer Graphics; January 2023, Vol. 29 Issue: 1 p756-766, 11p |
| Abstrakt: |
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. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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