BIGDATA: Mid-Scale: DA : Collaborative Research Big Tensor Mining Theory

BIGDATA：中型：DA：协作研究大张量挖掘理论

• 批准号: 8599832
• 负责人: CHRISTOS FALOUTSOS
• 金额: $ 14.98万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8599832
• 关键词: Address; Algorithms; Brain; Brain Part; Brain scan; Capital; Collaborations; Coupled; Data; Data Set; Discipline; Doctor of Philosophy; Education; Explosion; Female; Functional Magnetic Resonance Imaging; Human; Instruction; Internet; Maps; Memory; Methods; Mining; Pattern; Reading; Recommendation; Research; Running; Side; Students; System; Training; Triplet Multiple Birth; Washington; graduate student; language processing; phrases; real world application; software development; statistics; theories; tool

DESCRIPTION (provided by applicant): Given triplets of facts (subject-verb-object), like ('Washington' 'is the capital of 'USA'), can we find patterns, new objects, new verbs, anomalies? Can we correlate with brain scans, to discover which parts of the brain get activated, say, by tool-like nouns ('hammer'), or action-like verbs ('run')? We propose three research thrusts: i) Motivating Applications: Real-world tera-scale applications, including 'read-the-web', NeuroSemanticslarge recommendation systems ii) New theory and methods for big sparse tensor and coupled tensor/matrix factorization; and iii) Scalability to tera- and peta-byte data, using the map-reduce paradigm and extending it to multi-core settings. Intellectual Merit. We will be the first to address scalability issues for tensor and coupled tensor/matrix factorizations. We will leverage our Pegasusmining system which runs on Hadoop, and we will carefully exploit sparsity to avoid intermediate data explosion. On the theory/methods side, we propose a brand new multi-way compressed sensing framework for tensors and coupled tensor/matrix data that (a) dramatically redijces complexity and memory requirements, (b) is amenable to map-reduce and multi-core computation and (c) allows principled imputation of missing values. Heavily motivated by the above applications and influenced by their needs, our principled and scalable algorithms will enable new discoveries. Education: Graduate students in CS and ECE will be trained in a cross-disciplinary topic at the confluence of the two disciplines. The PIs have a record of successful collaboration, and the co-PIs bring together complementary strengths in theory, applications, and software development (METIS). They have also routinely involved female Ph.D. students and undergraduates in their research. RELEVANCE (See instructions): The proposed research seeks to develop a better understanding of human language processing, by relating fMRI and MEG human brain activity during reading to very large scale corpus statistics of the words and phrases being read. Focusing on scalability, we will study large datasets, which are outside the capabilities of typical, current methods.

描述(申请人提供)：给出三元组的事实(主语-动词-宾语)，如(‘华盛顿’是‘美国’的大写)，我们能找到模式、新宾语、新动词、异常吗？我们能通过脑部扫描来发现大脑的哪些部分被激活了吗，比如，类似工具的名词(“锤子”)或类似动作的动词(“跑”)？我们提出了三个研究方向：i)激发应用：真实世界的万亿级应用，包括‘网络阅读’、神经语义和大型推荐系统；ii)大型稀疏张量和耦合张量/矩阵因式分解的新理论和方法；iii)TB和Peta字节数据的可伸缩性，使用map-Reduce范式并将其扩展到多核环境。智力上的功绩。我们将是第一个解决张量和耦合张量/矩阵分解的可伸缩性问题的人。我们将利用我们在Hadoop上运行的Pegasus挖掘系统，并将谨慎地利用稀疏性来避免中间数据爆炸。在理论和方法方面，我们提出了一种全新的用于张量和耦合张量/矩阵数据的多路压缩感知框架，该框架(A)显著降低了复杂性和存储需求，(B)服从于映射约简和多核计算，(C)允许原则性地归因于缺失值。我们的原则性和可伸缩性算法受到上述应用程序的强烈激励和他们需求的影响，将使新的发现成为可能。教育：计算机科学和幼儿教育专业的研究生将在这两个学科的交汇处接受跨学科专题的培训。PI有成功协作的记录，共同PI在理论、应用程序和软件开发(METIS)方面具有互补优势。他们还经常邀请女博士生和本科生参与他们的研究。相关性(见说明)：拟议的研究试图通过将阅读过程中的功能磁共振成像和脑磁图人脑活动与正在阅读的单词和短语的大规模语料库统计数据联系起来，更好地理解人类的语言处理。关注可伸缩性，我们将研究大数据集，这些数据集超出了典型的当前方法的能力。