CohortVA: A Visual Analytic System for Interactive Exploration of Cohorts based on Historical Data

Wei Zhang, Jason Kamkwai Wong, Xumeng Wang, Youcheng Gong, Rongchen Zhu, Kai Liu, Zihan Yan, Siwei Tan, Huamin Qu, Siming Chen, Wei Chen

View presentation:2022-10-20T15:45:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T15:45:00Z
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We present CohortVA, an interactive visual analytic system for historians to identify and explore historical cohorts. Given an initial group of figures, the cohort identification model extracts their common features and identifies potential cohorts to improve the research efficiency. CohortVA’s visual interface provides various supporting information for historians to cross-check those results, fostering trust in the system and a deeper understanding of cohorts. Case studies and historian interviews demonstrate our system’s usefulness and effectiveness. We summarized the learned lessons and design implications, which we believe will guide system designers in dealing with historical data and working with historians.

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Abstract

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.