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.

计算机和人类网络产生的丰富信息为网络分析创造了令人兴奋的机会，即通过挖掘由各种监视器收集的大量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，必须解决几个重大挑战：（i）可扩展性。计算机和人类网络的巨大规模使得以可扩展的方式分析大量网络数据集变得具有挑战性。 （二）复杂性。 现实世界的网络数据集是复杂的，往往违反了现有分析技术的操作条件。 （三）稳健性。 异常和不完美在现实世界的网络数据集中很常见。 ㈣多样性。 网络分析通常需要从具有不同特征和数据质量的不同数据源中挖掘信息。本研究通过开发一系列新颖的压缩感知技术来有效利用现实世界网络数据集中存在的结构和冗余来解决上述挑战，包括：- （i）聚类谱图嵌入，一种新的技术，用于将大规模图减少到更小的图，同时保留原始图的基本聚类和谱信息，---（ii）透镜分解，一种将网络数据矩阵准确分解为低秩矩阵、误差项、噪声矩阵和稀疏矩阵的新方法，以及---（iii）多源谱学习，一种有效整合来自不同数据源的信息的新框架。 该研究有望显着提高分析大规模网络数据集的能力。 由此产生的工具和技术在商业、信息技术、网络和网络安全方面具有潜在的应用。 最后，研究包括一个重要的教育和培训部分。 研究成果将纳入本科和研究生课程以及外联活动。