CIF: Small: Compressive Network Analytics

CIF：小型：压缩网络分析

• 批准号: 1117009
• 负责人: Yin Zhang
• 金额: $ 50万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117009&HistoricalAwards=false
• 关键词: CIF; Small; Compressive; Network; Analytics

The rich information generated by computer and human networks creates exciting opportunities for network analytics, namely, the process of gaining knowledge and insights by mining a large amount of network data collected by a diverse set of monitors. To enable effective network analytics, several significant challenges must be addressed:--- (i) Scalability. The enormous scale of computer and human networks makes it challenging to analyze massive network datasets in a scalable fashion. --- (ii) Complexity. Real-world network datasets are complex and often violate the operational conditions of existing analysis techniques. --- (iii) Robustness. Anomalies and imperfections are common in real-world network datasets. --- (iv) Diversity. Network analytics often requires mining information from diverse data sources with different characteristics and data quality.This research addresses the above challenges by developing a series of novel compressive sensing techniques to effectively exploit the presence of structure and redundancy in real-world network datasets, including:--- (i) clustered spectral graph embedding, a novel technique for reducing a massive graph to a much smaller graph while preserving essential clustering and spectral information of the original graph,--- (ii) LENS decomposition, a novel method for accurately decomposing a network data matrix into a Low-rank matrix, an Error term, a Noise matrix, and a Sparse matrix, and --- (iii) multi-source spectral learning, a novel framework for effectively integrating information from diverse data sources. The research promises to significantly enhance the ability to analyze massive network datasets. The resulting tools and techniques have potential applications in business, information technology, networking and cyber security. Finally, the research includes a significant education and training component. The research results will be integrated into both undergraduate and graduate curricula as well as outreach activities.

计算机和人类网络生成的丰富信息为网络分析创造了令人兴奋的机会，即通过挖掘大量由各种监视器收集的网络数据来获得知识和见解的过程。 为了实现有效的网络分析，必须解决一些重大挑战：---（i）可伸缩性。计算机和人类网络的巨大规模使得以可扩展方式分析大型网络数据集的挑战。 ---（ii）复杂性。 现实世界网络数据集很复杂，并且经常违反现有分析技术的操作条件。 ---（iii）鲁棒性。 在现实世界网络数据集中，异常和瑕疵很常见。 ---（iv）多样性。 Network analytics often requires mining information from diverse data sources with different characteristics and data quality.This research addresses the above challenges by developing a series of novel compressive sensing techniques to effectively exploit the presence of structure and redundancy in real-world network datasets, including:--- (i) clustered spectral graph embedding, a novel technique for reducing a massive graph to a much smaller graph while preserving essential clustering and spectral information of the原始图，---（ii）镜头分解，一种新颖的方法，用于将网络数据矩阵准确分解为低级别矩阵，误差项，噪声矩阵和稀疏矩阵，以及----（iii）多源光谱学习，一种新的框架，以有效地集成了来自不同数据源的信息。 该研究有望显着增强分析大规模网络数据集的能力。 最终的工具和技术在业务，信息技术，网络和网络安全方面具有潜在的应用。 最后，研究包括重要的教育和培训部分。 研究结果将纳入本科和研究生课程以及外展活动。