Mixed-Initiative Visual Text Analytics: Data-driven Views and Analytic Guidance

可视化分析是一门将数据处理、信息可视化和软件相结合的科学，在数据分析过程中为人们提供支持。视觉分析的优势来自于让计算机进行与其能力相适应的工作，如对数据进行计数、聚类和批量处理，而人类分析师则从事假设形成、推理、证据收集和决策的工作。用可视化开始分析数据的传统方法是使用高级别的可视摘要来“概述”数据。然而，随着数据规模的增长，以有效的方式设计概述越来越具有挑战性。对非常大的数据的概述，比如一个城市一年内的所有推文，往往过于杂乱，无法揭示任何有趣的东西。因此，与其从概述开始，不如建议一个分析的起点，比如一个Twitter用户，或者一天的推文。拟议的研究将利用计算能力来创建数据驱动的起点建议，用于分析和指导下一步的工作。 在多大程度上发挥计算机在分析数据方面的作用，这是一个权衡。一方面，自动突出显示或隐藏数据细节会带来决策被算法偏向的潜在风险。我认为，让分析师承担不提供分析指导的可视化负担同样是不合适的，因为它忽视了计算系统自动检测趋势和潜在感兴趣区域的能力。面对全面审查的分析人士可能不知道该去哪里，在开始调查时可能会遇到挫折。通过提出一个有趣的起点，分析师可能会更快地达到深度参与分析任务的状态。因此，这项建议提出了一个研究计划，以调查视觉分析中人机关系的联系：控制应该在哪里，什么类型的指导是可能的和有帮助的，以及指导应该在什么时候提供？在所有这些调查中，我设想保留由人负责的模式，建议和指导可以被忽略或停用，类似于手机上的“自动完成”功能。 本研究中的问题将使用文本和文献数据进行调查。文本数据具有重要的经济和社会意义，每天通过电子邮件、商业报告、书籍、新闻、法律程序等产生海量的文本数据。通过研究过程，10名学生将接受备受欢迎的数据科学技能的专业培训。这项研究将引入新的视觉分析实践，实现分析师和算法更紧密的耦合。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规模数据集获得洞察力的能力。

Visual analytics is the science of combining data processing, information visualization, and software to support people in the process of data analysis. The strength of visual analytics comes from having computers do work appropriate to their capabilities, such as counting, clustering, and bulk processing of data, while human analysts do the work of hypothesis formation, reasoning, evidence gathering, and decision making. The traditional way to start analyzing data with visualizations is to "overview" the data using high-level visual summaries. However, as data scales have grown, overviews are increasingly challenging to design in an effective way. Overviews of very large data, such as all the Tweets in a city over a year, are often too cluttered to reveal anything interesting. So, rather than starting with an overview, it may be better to suggest a starting point for analysis, such as a single Twitter user, or tweets from a single day. The proposed research will harness computing power to create data-driven suggestions of starting points for analysis and guidance for next steps. There is a trade-off around how far to take the role of computers in analyzing data. On one side, automatic highlighting or hiding of data details introduces potential risk of decisions being biased by algorithms. I argue that burdening analysts with visualizations which do not provide analytic guidance is equally inappropriate, as it ignores the ability of computational systems detect trends and regions of potential interest automatically. Analysts faced with general overviews may not know where to go, and may experience frustration as they start an investigation. By suggesting an interesting place to start, an analyst may more quickly achieve a state of deep engagement in analytic tasks. Thus, this proposal presents a program of research to investigate the nexus of the human-computer relationship in visual analytics: where should the control lie, what sorts of guidance are possible and helpful, and when should guidance be provided? In all of these investigations, I imagine retaining a human-in-charge paradigm, where suggestions and guidance can be ignored or deactivated, similar to the `autocomplete' functions on a mobile phone.  The questions in this research will be investigated using text and document data. Text data is economically and socially important, and generated in enormous volumes daily, through emails, business reports, books, news, legal proceedings, and more. Through the research process, 10 students will receive specialized training in highly sought-after data science skills. This research will introduce new visual analytic practices, achieving a closer coupling of analysts and algorithms.  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.