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

混合主动视觉文本分析：数据驱动的视图和分析指导

• 批准号: RGPIN-2015-03916
• 负责人: Collins, Christopher
• 金额: $ 2.62万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=708417
• 关键词: Mixed; Initiative; Visual; Text; Analytics

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.

视觉分析是将数据处理，信息可视化和软件结合起来，以支持人们在数据分析过程中的科学。视觉分析的强度来自使计算机确实适合其功能的工作，例如对数据的计数，聚类和批量处理，而人类分析师进行了假设形成，推理，证据收集和决策的工作。开始使用可视化数据分析数据的传统方法是使用高级视觉摘要“概述”数据。但是，随着数据量表的增长，概述越来越有效地设计。非常大数据的概述，例如一年多的城市中的所有推文，通常都太混乱了，无法揭示任何有趣的东西。因此，与其从概述开始，不如提出一个分析的起点，例如单个Twitter用户或一天的推文。拟议的研究将利用计算能力来创建数据驱动的起点建议，以进行分析和下一步指导。 关于计算机在分析数据中的作用多远，这是一个权衡取舍。一方面，自动突出显示或隐藏数据详细信息会引入算法偏向决策的潜在风险。我认为，具有不提供分析指南的可视化分析师同样不合适，因为它忽略了计算系统的能力自动检测潜在兴趣的趋势和区域。面对一般概述的分析师可能不知道该去哪里，并且在开始投资时可能会感到沮丧。通过建议一个有趣的起点，分析师可能会更快地实现对分析任务的深入参与状态。这是一项研究计划，以调查视觉分析中人类计算机关系的联系：控制控制在哪里，可以提供什么样的指导和有益的指导，何时应提供指导？在所有这些投资中，我认为保留了负责人的范式，可以忽略或停用建议和指导，类似于手机上的“自动完成”功能。 这项研究中的问题将使用文本和文档数据进行研究。文本数据在经济和社会上很重要，每天通过电子邮件，商业报告，书籍，新闻，法律程序等产生庞大的数量。通过研究过程，有10名学生将获得高度有意义的数据科学技能的专业培训。这项研究将介绍新的视觉分析实践，从而实现分析师和算法的更紧密耦合。结果有望提高加拿大人从经济和社会上重要的大规模数据集中获得从商业智能到健康信息的大型数据集的能力。