III: Small: Supporting Investigative Analysts and Researchers in Sense-making across Large Document Collections through Visual Analytics

III：小型：通过可视化分析支持调查分析师和研究人员在大型文档集合中理解意义

• 批准号: 0915788
• 负责人: John Stasko
• 金额: $ 48.97万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915788&HistoricalAwards=false
• 关键词: III; Small; Supporting; Investigative; Analysts

People routinely encounter and seek to make sense of large collections of data that include both unstructured reports or stories and loosely-structured logs or spreadsheets. In many cases, information of relevance is scattered about a large number of documents and it is the task of an analyst to read the documents and "put the pieces together." For instance, police investigators when sifting through a multitude of observations, case reports, and witness testimonies must develop a coherent view of the events that really occurred. Academic researchers investigating a new domain pour over large numbers of paper abstracts, citations, and articles to develop a better understanding of the state of work in that area. The process of connecting individual pieces of information such as those discussed above into a more coherent narrative is a component of investigative analysis, the main focus of this project. One common element of analytic sense-making activities is that they are cognitively very challenging, frequently involving large collections of data that tax a person's memory, deduction, reasoning, and general analytic capabilities. Investigative analysis today is made even more challenging by the ever-increasing torrent of data available in a world where one can access vast databases and conduct internet searches that in seconds return a quantity of documents no human can read and assimilate in a reasonable amount of time. But technological means of augmenting human memory and analytic reasoning hold great potential as investigative aids. This project explores the development of computational systems to make investigative analysts more effective and more efficient. The PI's approach centers on providing multiple visual representations of the individual pieces of data gathered during the investigation, to help highlight connections or potential connections among them and to help analysts determine the next pieces of data to examine from a large collection of evidence. The PI will draw upon his experience in information visualization and visual analytics to design and create a system to help analysts, and upon his experience in human-computer interaction to evaluate whether the system is effective. The work will include fundamental research on challenges such as the representation of reliability and uncertainty in a visualization display, the development of collaborative system capabilities so that analysts can work together, and the integration of sophisticated automated textual analysis capabilities with the human-directed exploration approach that visual interfaces provide. Careful evaluation of all the new analytic capabilities will accompany their design as well.Broader Impacts: Investigative analysis is a fundamental activity in law enforcement and in intelligence activities that are important to our national security. This project will invent next-generation visual analytic techniques and technologies that can be used to develop investigative analysis systems in the future. Other domains such as news reporting, academic research, and business intelligence also require investigative analysis, so this project has the potential to impact those fields as well.

人们经常会遇到并试图理解大量的数据，其中包括非结构化的报告或故事以及结构松散的日志或电子表格。 在许多情况下，相关的信息分散在大量的文档中，分析人员的任务是阅读这些文档并“把碎片放在一起”。" 例如，警方调查人员在筛选大量的观察结果、案件报告和证人证词时，必须对真正发生的事件形成一个连贯的观点。 研究一个新领域的学术研究人员会阅读大量的论文摘要、引文和文章，以更好地了解该领域的工作状况。 将上文所述的各种信息联系起来，形成更加连贯的叙述，这是调查分析的一个组成部分，也是本项目的主要重点。 分析性意义创造活动的一个共同点是，它们在认知上非常具有挑战性，经常涉及大量的数据，这些数据对一个人的记忆、推理、推理和一般分析能力造成了负担。 当今世界上不断增加的数据洪流使调查分析更具挑战性，人们可以访问庞大的数据库并进行互联网搜索，在几秒钟内返回大量人类无法在合理的时间内阅读和吸收的文件。 但是，增强人类记忆和分析推理的技术手段作为调查辅助手段具有巨大的潜力。 这个项目探索了计算系统的发展，使调查分析更有效，更高效。 PI的方法集中在提供调查期间收集的单个数据的多个视觉表示，以帮助突出它们之间的联系或潜在联系，并帮助分析师从大量证据中确定下一个要检查的数据。 PI将利用他在信息可视化和可视化分析方面的经验来设计和创建一个系统来帮助分析师，并利用他在人机交互方面的经验来评估该系统是否有效。 这项工作将包括对挑战的基础研究，例如在可视化显示中表示可靠性和不确定性，开发协作系统功能，以便分析师可以共同工作，以及将复杂的自动文本分析功能与可视化界面提供的人类导向的探索方法相结合。 更广泛的影响：调查分析是执法和情报活动中的一项基本活动，对我们的国家安全至关重要。 该项目将发明下一代视觉分析技术和技术，可用于开发未来的调查分析系统。 新闻报道、学术研究和商业智能等其他领域也需要调查分析，因此该项目也有可能影响这些领域。