CGV: Small: Model-Driven Visual Analytics on Streams

CGV：小型：模型驱动的流可视化分析

• 批准号: 1117139
• 负责人: Elke Rundensteiner
• 金额: $ 49.99万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117139&HistoricalAwards=false
• 关键词: CGV; Small; Model; Driven; Visual

Risk analysis and monitoring of continuously arriving data are required by a wide range of domains critical to our individual and societal well-being, from the military to the financial sector, from medicine to homeland security, from man-made to natural disasters. These application areas use patterns or models to differentiate behavior of varying degrees of importance or risk, both in terms of the value for a particular variable, relationships between different variables, and changes across time. The objective of this project is the design, development, and assessment of visual analytics technology to support the real-time interactive analysis of data streams that focuses on creating and using models of stream behavior to identify instances of potentially important activities and risks in the data. The research breaks new ground in the design of innovative exploration techniques of high-volume streaming data, with a focus on visualization and interactions of competing and complementary models extracted from the stream data and of change descriptions derived from each. Novel technology includes computational and visual methods for model formation, management, and model change analysis. Efficient indexing and compression of the history of how data and models change provide analysts with the ability to compare and contrast similar events over long periods of time. Linked views of the different information spaces (data, model, change, and history), and powerful interaction tools to support exploration and model building enable analysts to confirm the expected and uncover the unexpected. The results of this project are expected to have significant impact is a wide range of domains that rely on extracting information from real-time digital data, including finance, medicine, and homeland security. In particular, tools are developed for solving financial risk and fraud detection problems critical to the economic well-being of individuals and organizations. Educational material from using the technology in courses are freely available, along with source code and data sets, via the project web page (http://davis.wpi.edu/~xmdv).

从军事到金融部门，从医学到国土安全，从人为灾害到自然灾害，对个人和社会福祉至关重要的广泛领域都需要对不断到达的数据进行风险分析和监测。这些应用程序领域使用模式或模型来区分重要性或风险程度不同的行为，包括特定变量的值、不同变量之间的关系以及随时间的变化。该项目的目标是设计、开发和评估可视化分析技术，以支持数据流的实时交互分析，重点是创建和使用流行为模型，以识别数据中潜在重要活动和风险的实例。该研究在设计大容量流数据的创新探索技术方面开辟了新路，重点是从流数据中提取的竞争和互补模型的可视化和交互，以及从每个模型中派生的变化描述。新技术包括用于模型形成、管理和模型变化分析的计算和可视化方法。对数据和模型变化的历史进行有效的索引和压缩，使分析人员能够在很长一段时间内比较和对比类似的事件。不同信息空间（数据、模型、变更和历史）的链接视图，以及支持探索和模型构建的强大交互工具，使分析人员能够确认预期的结果并发现意外的结果。预计该项目的成果将对依赖于从实时数字数据中提取信息的广泛领域产生重大影响，包括金融、医学和国土安全。特别是，开发工具来解决对个人和组织的经济福祉至关重要的金融风险和欺诈检测问题。通过项目网页（http://davis.wpi.edu/~xmdv），可以免费获得在课程中使用该技术的教育材料，以及源代码和数据集。