III-COR: Collaborative Research: Mining Biomedical and Network Data Using Tensors

III-COR：协作研究：使用张量挖掘生物医学和网络数据

• 批准号: 0705359
• 负责人: Christos Faloutsos
• 金额: $ 30.8万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705359&HistoricalAwards=false
• 关键词: III; COR; Collaborative; Research; Mining

IIS 0705359, IIS 0705215III-COR: Collaborative Research: Mining Biomedical and Network Data Using Tensors Christos Faloutsos ([e-mail]) CMU Vasileios Megalooikonomou ([e-mail]) Temple Univ.Given a large collection of functional Magnetic Resonance (fMR) images over time,how can one find patterns and correlations? Similarly, given a never-ending stream of network traffic information, how can one monitor for anomalies, intrusions, and potential failures? The main idea behind this proposal is to treat both problems using the theory of tensors. Despite the seemingly wide differences in the two settings, they both boil down to finding patterns in multidimensional arrays, sparse or dense. Tensors are exactly generalizations of matrices, and correspond roughly to ``DataCubes'' of data mining. Matrix analysis and decompositions are part of the standard toolbox for data mining, providing methods for dimensionality reduction, pattern discovery and``hidden variable'' discovery. Extending these tools to higher dimensionalities is valuable and tensors provide the tools to do this generalization. However, these tools have not yet been put to use in large volume data mining. This is the main contribution of this proposal. The investigators propose (a) to design tensor decomposition algorithms that scale for large datasets,with special attention to sparse datasets, and to never-ending streams of data and (b) to apply them on two driving applications, fMRI data analysis and networkdata analysis. The investigators propose to analyze large volumes of fMRI data performingthe following sub-tasks: cluster voxels with similar behavior over time fora given subject and/or task or across subjects and/or tasks, classify patterns of brain activity, and detect lag correlationsand spatio-temporal patterns among fMRI time sequences. The investigators also propose to perform the following inter-related tasks on multiple GigaBytes of network flow data: anomaly detection, pattern discovery, and compression.Both of these applications are important for medicine, health management,and for computer and national security. Analysis of fMRI data can help understandinghow the brain functions, which parts of the brain collaborate with what other parts, and whether there are variations across subjects and across task-related activities. For the network traffic monitoring setting, fast detection of anomalies is important,to spot malware, port-scanning attempts, and just plain non-malicious failures.The educational goals include incorporating the research findings in advanced graduate courses at CMU (15-826) and at Temple (9664, 9665)and proposing tutorials in leading conferences in databases, data mining and bio-informatics audiences.For further information see the web page: http://knight.cis.temple.edu/~vasilis/research/tensors.html

IIS 0705359，IIS 0705215III-COR：合作研究：使用张量Christos christos faloutsos（[e-mail]）CMU Vasileios Megalooikonomou（[e-mail] comentional timemry timemrance fackional timeprance（FMIANITAL）timemone（FMIANDER），使用张量cmu vasileios timemone（FM），找到模式和相关性？同样，考虑到无休止的网络流量信息流，如何监视异常，入侵和潜在故障？该提案背后的主要思想是使用张量理论来治疗这两个问题。尽管这两种设置似乎存在很大的差异，但它们都归结为在稀疏或致密的多维阵列中找到图案。张量完全是矩阵的概括，并且大致对应于数据挖掘的``数据存储''。 矩阵分析和分解是数据挖掘的标准工具箱的一部分，为降低维度降低，模式发现和“隐藏变量”的发现提供了方法。将这些工具扩展到更高的维度是有价值的，张量提供了进行这种概括的工具。但是，这些工具尚未用于大量数据挖掘。这是该提议的主要贡献。研究人员建议（a）设计张量分解算法，以规模大型数据集扩展，并特别注意稀疏数据集，以及无休止的数据流以及（b）将它们应用于两个驾驶应用程序，fMRI数据分析和网络数据分析。研究人员建议分析大量fMRI数据执行以下子任务：群集体素随着时间的流逝和/或任务和/或任务或跨受试者和/或任务，对大脑活动的模式进行分类，并检测fMRI时间序列之间的滞后相关性和时空模式。调查人员还建议对网络流数据的多个千兆字节执行以下相互关联的任务：异常检测，模式发现和压缩。这些应用程序对医学，健康管理以及计算机和国家安全都很重要。 FMRI数据的分析可以帮助您了解大脑功能，大脑的哪些部分与其他部分合作，以及在受试者之间以及与任务相关的活动之间是否存在差异。 For the network traffic monitoring setting, fast detection of anomalies is important,to spot malware, port-scanning attempts, and just plain non-malicious failures.The educational goals include incorporating the research findings in advanced graduate courses at CMU (15-826) and at Temple (9664, 9665)and proposing tutorials in leading conferences in databases, data mining and bio-informatics audiences.For further information请参阅网页：http：//knight.cis.temple.edu/~vasilis/research/tensors.html