Guidance in Visual Analytics through Mixed-Initiative and Implicit Interaction

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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.