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

可视化分析是一门结合数据处理、信息可视化和软件来支持人们进行数据分析的科学。视觉分析的强大之处在于让计算机完成与其能力相适应的工作，例如计数、聚类和数据的批量处理，而人类分析师则完成假设形成、推理、证据收集和决策制定的工作。使用可视化开始分析数据的传统方法是使用高级可视化摘要来“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，比如一个城市一年来的所有推文，往往过于杂乱，无法揭示任何有趣的东西。因此，与其从概览开始，不如建议一个分析的起点，例如单个Twitter用户或一天中的tweet。拟议的研究将利用计算能力来创建数据驱动的分析起点建议和下一步的指导。***在分析数据方面，计算机的作用发挥到什么程度是需要权衡的。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见的潜在风险。我认为，让分析人员承担不能提供分析指导的可视化负担同样是不合适的，因为它忽略了计算系统自动检测趋势和潜在兴趣区域的能力。面对总体概况的分析师可能不知道该去哪里，在开始调查时可能会感到沮丧。通过建议一个有趣的起点，分析人员可以更快地达到深入参与分析任务的状态。因此，本提案提出了一项研究计划，以调查视觉分析中人机关系的联系：控制应该在哪里，什么样的指导是可能的和有用的，什么时候应该提供指导？在所有这些调查中，我设想保留一种由人负责的模式，在这种模式中，建议和指导可以被忽略或禁用，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档数据进行调查。文本数据在经济上和社会上都很重要，每天通过电子邮件、商业报告、书籍、新闻、法律程序等产生大量文本数据。通过研究过程，10名学生将接受非常抢手的数据科学技能的专门培训。这项研究将引入新的可视化分析实践，实现分析师和算法的更紧密耦合。研究结果有望提高加拿大人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中获得见解的能力

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.**