III: Small: Collaborative Research: Anomaly Detection in Graph Streams

III：小：协作研究：图流中的异常检测

• 批准号: 1318913
• 负责人: Lawrence Holder
• 金额: $ 24.98万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1318913&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Anomaly

The main objective of this project is to develop scalable algorithms for learning normative patterns and anomalies in graph streams, where the patterns are known, unknown but fixed, or changing over time. The project team is pursuing several techniques, including partitioning the graph over time, processing only the changes to the graph over time, and parallel implementations on high-performance computing platforms. They are evaluating the effectiveness and efficiency of these algorithms in terms of expected data sizes, data rates, and recall/precision using several real-world, large, dynamic datasets as well as synthetic data. They are also evaluating the discovered patterns and anomalies for their significance in the target domains. This research is advancing the knowledge and understanding of how to efficiently process large, high-rate data streams represented as a graph in order to learn structural patterns and detect structural anomalies in real time. The algorithms developed under this project represent a new level of scalability that is necessary to address today?s massive, dynamic data environments, as well as users' needs to quickly discover actionable intelligence in the form of trends and anomalies. This project impacts the scientific research community by advancing the state-of-the-art in mining graphs for patterns and anomalies in large, dynamic data streams, and disseminating these research results via publications, software tools and data to be provided on the project website. This project also impacts education via the inclusion of research results into existing courses at the teams' institutions, and the dissemination of these curricular materials via the project website. The project supports the research training of two graduate students, utilizing recruiting efforts from underrepresented groups to assist in the selection of these students. The project benefits society by providing efficient and effective tools for detecting patterns and anomalies in data that can lead to new discoveries in a variety of domains where large amounts of dynamic data are available, including national security, cyber-security, and social media.For further information see project website: http://ailab.wsu.edu/adgs

该项目的主要目标是开发可扩展的算法，用于学习图形流中的规范模式和异常，其中模式是已知的，未知的但固定的，或随时间变化的。该项目团队正在寻求几种技术，包括随着时间的推移对图进行分区，只处理随着时间的推移对图的更改，以及在高性能计算平台上的并行实现。他们正在使用几个真实世界的大型动态数据集以及合成数据来评估这些算法在预期数据大小，数据率和召回率/精度方面的有效性和效率。他们还在评估发现的模式和异常在目标领域的重要性。这项研究正在推进知识和理解如何有效地处理大型，高速率的数据流表示为一个图形，以学习结构模式和检测结构异常在真实的时间。在这个项目下开发的算法代表了一个新的水平的可扩展性，是必要的，以解决今天？的海量动态数据环境，以及用户快速发现趋势和异常形式的可操作情报的需求。该项目通过推进在大型动态数据流中挖掘模式和异常图的最新技术，并通过出版物、软件工具和项目网站上提供的数据传播这些研究成果，对科学研究界产生影响。该项目还通过将研究成果纳入小组所在机构的现有课程以及通过项目网站传播这些课程材料，对教育产生影响。该项目支持对两名研究生进行研究培训，利用从代表性不足的群体中征聘的努力来协助挑选这些学生。该项目通过提供高效和有效的工具来检测数据中的模式和异常，从而使社会受益，这些数据可以在各种领域中获得大量动态数据，包括国家安全，网络安全和社交媒体。欲了解更多信息，请访问项目网站：http://ailab.wsu.edu/adgs