We propose the Active Visual Analytics technique (ActiveVA), an augmentation of interactive visualizations with active search to aid data foraging and information extraction. We accomplish this by integrating an active search algorithm into the visual interface, which leads users to explore promising data points on a visualization and updates suggestions upon observing user feedback. Using a fictitious epidemic dataset published by the VAST community, we conduct two evaluations. First, we present simulation results that demonstrate the effectiveness of active search in data discovery. Second, we show how a human-computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study. Finally, we outline open research questions regarding human factors in active search and the implications of this line of research in real-world scenarios such as visual drug discovery and intelligence analysis.

翻译：我们建议采用主动视觉分析技术(ApentVA),通过积极搜索增强互动可视化,以帮助数据采集和信息提取。我们通过将积极搜索算法纳入视觉界面,使用户探索视觉化的有希望的数据点,并在观察用户反馈时更新建议。我们使用VAST社区公布的虚构的流行病数据集,进行两项评估。首先,我们提出模拟结果,以显示在数据发现中积极搜索的有效性。第二,我们展示基于积极VA的人体计算机伙伴关系如何在交互式视觉探索和发现过程中产生更有意义的互动,同时进行众源用户研究。最后,我们概述了关于积极搜索中的人类因素以及这一研究线在视觉药物发现和情报分析等现实世界情景中的影响的公开研究问题。