MRI: Acquisition of Tera-Scale Data Analytic Platforms for Research in the Combinatorial and Graph Sciences Consortium

提案#：CNS07-23060PI(S)：Gloster，Jr.，Clay Boyack，Kevin W.；Davidson，George S.；Ramanathan，Murali；Zacharski，Ron Institution：Howard University Washington，DC 20059-0001标题：磁共振成像/访问：Terascale Data Analytic Platform for Research in the Combinative and Graph Science Consortium项目建议：该项目为组合和图形科学的研究获取分析平台，解决了从海量数据集中提取信息的关键问题。所要求的仪器是一个用于生产的大型系统(Netezza Performance Server(S))，将安装在霍华德和桑迪亚国家实验室，为以下科学项目服务：DNA结合机制、蛋白质结构预测、桑迪亚大学计算科学的数据挖掘、纽约大学布法罗分校的多维可视化以及国家密歇根州立大学的自然语言处理。这项工作是为了应对目前让科学界不堪重负的数据洪灾/海啸/爆炸/雪崩。数据密集型计算机资源，即国家TB级数据分析中心(NTDAC)，能够以更低的成本分析兆兆级数据集，速度比目前的替代方案快10到100倍。随着时间的推移，NTDAC预计将存储和分析Peta字节的原始数据，具有备份和支持能力，并使用速度更快、更易于使用的并行关系数据仓库设备技术。最初的重点是开发大规模并行关系数据库应用程序技术，使计算生物学、化学、物理、文本挖掘、书目耦合和自然语言处理中的问题得以解决。广泛的影响：该基础设施通过更快的数据分析使所有科学领域受益。对海量数据集的询问使从业者和学生能够更快地提供/获得新知识，并创建所需的解决方案。此外，该机构是一所HBCU，少数族裔学生可能会从这一经历中受益。

Proposal #: CNS 07-23060PI(s): Gloster, Jr., Clay Boyack, Kevin W.; Davidson, George S.; Ramanathan, Murali; Zacharski, Ron Institution: Howard University Washington, DC 20059-0001Title: MRI/Acq.: Terascale Data Analytic Platforms for Research in the Combinatorial and Graph Sciences ConsortiumProject Proposed:This project, acquiring analytic platforms for research in combinatorial and graph sciences, attacks a critical problem, that of extracting information from massive sets of data. The requested instrumentation, a large system for production (Netezza Performance Server(s)), are to be housed at Howard and Sandia National Lab and serve scientific projects on DNA binding mechanisms, protein structure prediction, data mining for computational science at Sandia, multi-dimensional visualization at SUNY-Buffalo, and natural language processing at NMSU. The work responds to the data deluge/tsunami/explosion/avalanchethat presently overwhelms the scientific community. The data intensive computer resource, National Terabyte Data Analysis Centers (NTDAC), provides the ability to analyze tera-scale data sets 10 to 100 times faster than current alternatives at a lower cost. Over time, NTDAC is expected to house and analyze peta-bytes of raw data, to have back-up and support capabilities, and to use parallel relational data warehouse appliance technology that is orders of magnitude faster and easier to use. The initial focus, exploitation of massively parallel relational database appliance technology, enables solution problems in computational biology, chemistry, physics, text mining, bibliographic coupling, and natural language processing.Broader Impact: The infrastructure benefits all areas of science through faster data analysis. The interrogation of massive data sets allows practitioners and students to deliver/gain new knowledge at a more rapid pace, and to create needed solutions. Moreover, the institution is an HBCU, where minority students are likely to gain from the experience.