Guidance in Visual Analytics through Mixed-Initiative and Implicit Interaction

视觉分析是一门将数据处理和信息可视化相结合的科学，以支持人们理解数据。它的优势来自于让计算机从事与其能力相适应的工作，如计算和组织数据，而人类分析员则从事推理、证据收集和决策工作。长期以来，这种协作的计算方面的重点一直是数据挖掘、布局和显示大量信息。指导分析过程主要是人类分析员的领域。我的研究将探索软件在探索数据集时建议人类分析师采取哪些步骤的方法。许多人际交流都是通过隐含的方式进行的--肢体语言、语调、面部表情和用词。我们的外显交流得到了这些隐含线索的支持。隐含的暗示提供了很多机会来提高我们使用计算机系统的工作效率。例如，通过监视用户查看图表中一系列点的凝视，分析系统可以建议可能符合用户兴趣的其他未探索的项目，而无需明确询问，也无需用户明确表示他们的兴趣。这项研究将探索来自笔、触摸、虚拟现实中的手势和眼睛凝视的隐含线索。关于计算机在分析数据方面的作用有多大，存在一个权衡。自动突出显示或隐藏数据会带来算法偏差的风险。但在没有指导的情况下让分析师承担负担同样是不合适的，因为工作更困难，重要数据可能会被遗漏。我将调查这种权衡：控制应该在哪里，什么样的指导是可能的和有帮助的，以及应该在什么时候提供指导？文本分析将是调查的主要试验台。每天都会以多种语言生成数十亿页的文本：电子邮件、新闻、博客、评论、论坛、学术文章和商业报告。这项研究将产生一系列文本交互工具，将隐式交互、复杂的语言分析和可视化结合在一起，以揭示隐藏的信息，如主题、趋势和情感。我设想引入在线导航和使用文本集合的新方法，通过丰富和集中分析和阅读行为来减轻信息过载的负担。这项研究将引入新的可视化分析实践，实现分析师和算法更紧密的耦合。12名学员将接受备受欢迎的数据科学技能的专门培训。这些成果有望提高加拿大人从从商业智能到健康信息学等领域的经济和社会重要大型数据集获得洞察力的能力。这项研究融合了安大略省开放商业议程中的互动数字媒体和人工智能主题，直接解决了政府的优先事项。

Visual analytics is the science of combining data processing and information visualization to support people in understanding data. Its strength comes from having computers do work appropriate to their capabilities, such as counting and organizing data, while human analysts do the work of reasoning, evidence gathering, and decision-making. The focus of the computational aspect of this collaboration has long been on data mining, layout, and displaying large amounts of information. Guiding the analytic process has primarily been solely the domain of the human analyst. My research will investigate ways for software to advise human analysts on steps to take when exploring datasets. Much interpersonal communication occurs implicit means - body language, intonation, facial expression, and word choice. Our explicit communication is supported by these implicit cues. Implicit cues offer a lot of opportunity to improve the effectiveness of our work with computer systems. For example, by monitoring the gaze of a user looking at a sequence of points in a chart, an analytic system could suggest additional unexplored items which may fit the user's interest, without being explicitly asked, and without the user explicitly stating their interest. This research will explore implicit cues from pen, touch, gestures in virtual reality, and eye gaze.  There is a trade-off around how far to take the role of computers in analyzing data. Automatic highlighting or hiding of data introduces risk of algorithmic bias. But burdening analysts without guidance is equally inappropriate, as the work is more difficult, and important data may be missed. I will investigate this trade-off: where should the control lie, what sorts of guidance is possible and helpful, and when should guidance be provided? Text analytics will be the main testbed for the investigation. Billions of pages of text, in many languages, are produced daily: emails, news, blogs, reviews, discussion forums, academic articles, and business reports. This research will produce a collection of interactive tools for text which couple implicit interaction, sophisticated linguistic analysis, and visualization to reveal hidden information, such as topics, trends, and emotions. I envision introducing new ways to navigate and use text collections online, lessening the burden of information overload by enriching and focusing the acts of analysis and reading. This research will introduce new visual analytic practices, achieving a closer coupling of analysts and algorithms. 12 trainees will receive specialized training in highly sought-after data science skills. The outcomes promise to improve Canadians' ability to gain insights from economically and socially important large-scale datasets in domains from business intelligence to health informatics. Blending the interactive digital media and artificial intelligence themes of Ontario's Open for Business Agenda, this research directly addresses government priorities.