Data foraging routinely involves sifting through a large amount of irrelevant information in search of relevant data. In machine learning, the related task of active search considers the automated discovery of rare, valuable items from large data sets -- a setting that maps directly onto data foraging. Although there has been a long history of integrating similar assistive technologies into the visual analytics pipeline, we do not fully understand how these technologies impact human behavior or what factors might impact the machine partners' effectiveness. We frame data foraging as a sequential decision-making process and propose using active search as an assistive technology for accelerating discovery. We conduct a crowd-sourced user study to evaluate this human-machine partnership in data foraging and show that our approach results in higher throughput and more meaningful interactions during interactive visual exploration and discovery. Furthermore, we present evidence from a follow-up user study that the impact of incorporating assistive technology in visual tasks varies with interface design and task difficulty.

翻译：常规数据采集需要通过大量不相关的信息进行筛选,以寻找相关数据。在机器学习中,积极搜索的相关任务考虑的是自动发现大型数据集中的稀有、有价值的物品 -- -- 一种将直接映射成数据源的设置。虽然将类似的辅助技术纳入视觉分析管道的历史悠久,但我们不完全理解这些技术如何影响人类行为或哪些因素可能影响机器伙伴的效能。我们把数据设定为连续决策程序,并提议使用积极搜索作为加快发现速度的辅助技术。我们进行了众源用户研究,以评价这种在数据采集方面的人类机器伙伴关系,并表明我们的方法在互动视觉探索和发现过程中产生了更高的吞吐量和更有意义的互动。此外,我们从后续用户研究中提出证据,说明将辅助技术纳入视觉任务的影响因界面设计和任务困难而不同。