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MRI: Acquisition of a High Performance Big Data Analysis Platform

MRI: Acquisition of a High Performance Big Data Analysis Platform
MRI:收购高性能大数据分析平台
批准号:
1828521
负责人:
Nihat Altiparmak
金额:
$47.87万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-10-01 至 2020-09-30

项目摘要

项目成果

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中文摘要
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英文摘要
This project from the University of Louisville Research Foundation will support acquisition of a Big Data Analysis Platform (BDAP) with high performance data storage, networking, and processing capabilities. The platform will support research in multimedia, biomedicine, metagenomics, and health data analysis. In addition, the platform will allow general research on efficient management and analysis of big data. Moreover, strengthening scientific research across the university, the researchers will engage with K-12 students and teachers.The project will advance the state-of-the-art in big data management and analysis by enabling five research thrusts that will use the BDAP equipment purchased by this award. First, BDAP will provide improvement over existing big data management techniques by introducing self-optimizing and energy efficient big data platforms through dynamic data placement, retrieval, and reorganization algorithms, as well as enabling efficient big data analysis through novel heterogeneous and multi-source data clustering algorithms. Second, BDAP will allow experimentation with novel algorithms guided by deep neural networks to analyze big multimedia data. Third, BDAP will enable experimentation with new paradigms for integrating big biomedical data from multiple sources including image, genomic, quantitative, biological, and observational data. Fourth, BDAP will enable testing of techniques to generate, store, analyze and integrate large microbiome, health and socioeconomic data sets to determine the causal relationship between specific microbial profiles and human health via bioinformatics and data mining approaches. Finally, the project will contribute to the development of new statistical methods for analyzing high dimensional data for epigenetic, pharmacogenomics, and genome association studies. This in-house instrument enables energy consumption measurements and permanent storage for sensitive data sets of petabyte-range. While advancing engineering knowledge, the instrumentation supports computational, statistical, and bioengineering research, that applies engineering principles to several problems in biology and medicine.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(2)
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会议论文
DOI: 10.1109/cloudcom.2019.00046
发表时间: 2019-12
期刊: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
影响因子: --
作者: [B. Harris;Nihat Altiparmak]
通讯作者: B. Harris;Nihat Altiparmak
DOI: --
发表时间: 2020
期刊:
影响因子: --
作者: [B. Harris;Nihat Altiparmak]
通讯作者: B. Harris;Nihat Altiparmak
CC* Data Storage: Cardinal Academic Research Data Storage (CARDS)
CRII: CSR: Online Analysis of Disk I/O for Automatic Storage System Optimization
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