ACT SGER: Locating Sparse Events in High Speed Stream Data, with a Focus on Statistical Analysis

ACT SGER：定位高速流数据中的稀疏事件，重点是统计分析

• 批准号: 0346307
• 负责人: Xiaoming Huo
• 金额: $ 10万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0346307&HistoricalAwards=false
• 关键词: ACT; SGER; Locating; Sparse; Events

The PIs propose to study statistical problems that arise from identifying sparse events in high-speed stream sequences. Stream data are commonly encountered in many applications. Examples include (1) internet traffic data in internet pricing and network security, (2) the vast amount of information in communication networks and the demand of (terrorism) activity monitoring. These problems are closely related to the theme of Approaches to Combat Terrorism (ACT). These problems share some common properties: the events of interests are 'extremely' sparse; and the identification needs to be realized at a 'very' high speed. Moreover, in both network security and terrorist activity monitoring, false detection is far more disastrous than false alarms. It poses big challenges to both statisticians and computational scientists to find efficient methods to identify sparse events in stream data at high speed. The PIs propose to study the above problems, emphasizing the statistical modeling aspect, namely, what is the optimal modeling and computational strategy to solve these problems. The main challenge in stream data is the development of fast real time algorithms. Statisticians have developed powerful tools in sequential analysis, e.g., MCMC and other maximal likelihood based approaches. At the same time, the proposed problem has not been given much attention in statistics. New analysis needs to be done to integrate both optimality in mathematical statistics and efficiency in scientific computing. The PIs will start with two key ideas: a tree-based matching method and a special decision tree - cascade. They will benefit from interactions with researchers in other fields such as computer vision and computer security. The support will allow the PIs to train two graduate students to work in this area. The support will also help to jump start a larger effort on computationally efficient statistical modeling in stream data. The PIs will develop software for their algorithms and make it available over the internet.This award is supported jointly by the NSF and the Intelligence Community. The Approaches to Combat Terrorism Program in the Directorate for Mathematical and Physical Sciences supports new concepts in basic research and workforce development with the potential to contribute to national security.

PI建议研究在高速流序列中识别稀疏事件所产生的统计问题。流数据在许多应用中经常遇到。例如：（1）互联网定价和网络安全中的互联网流量数据，（2）通信网络中的大量信息和（恐怖主义）活动监控的需求。这些问题与“打击恐怖主义办法”的主题密切相关。这些问题有一些共同的性质：感兴趣的事件是“非常”稀疏的，和识别需要实现在一个“非常”的高速。此外，在网络安全和恐怖活动监测中，错误检测比错误警报更具灾难性。如何快速有效地识别流数据中的稀疏事件，对统计学家和计算科学家都提出了很大的挑战。PI建议研究上述问题，强调统计建模方面，即什么是解决这些问题的最佳建模和计算策略。流数据的主要挑战是快速真实的时间算法的开发。统计学家已经开发出强有力的序列分析工具，例如，MCMC和其他基于最大似然的方法。与此同时，所提出的问题在统计学上没有得到太多的关注。需要进行新的分析，以整合数理统计中的最优性和科学计算中的效率。PI将从两个关键思想开始：基于树的匹配方法和特殊的决策树-级联。他们将受益于与计算机视觉和计算机安全等其他领域研究人员的互动。这项支持将使PI能够培训两名研究生在这一领域工作。这种支持还将有助于启动流数据中计算效率高的统计建模的更大努力。PI将为他们的算法开发软件，并通过互联网提供。该奖项由NSF和情报界共同支持。数学和物理科学局的打击恐怖主义方案支持基础研究和劳动力发展方面的新概念，这些概念有可能促进国家安全。