MRI: Acquisition of a High Performance Big Data Analysis Platform

路易斯维尔大学研究基金会的这个项目将支持收购具有高性能数据存储、网络和处理能力的大数据分析平台(BDAP)。该平台将支持多媒体、生物医学、元基因组学和健康数据分析方面的研究。此外，该平台将允许对大数据的高效管理和分析进行一般性研究。此外，为了加强整个大学的科学研究，研究人员将与K-12的学生和教师接触。该项目将通过启用五个使用该奖项购买的BDAP设备的研究推进器，促进大数据管理和分析方面的最先进水平。首先，BDAP将通过动态数据放置、检索和重组算法引入自我优化和节能的大数据平台，并通过新颖的异构和多源数据集群算法实现高效的大数据分析，从而改进现有的大数据管理技术。其次，BDAP将允许在深度神经网络的指导下进行新算法的实验，以分析大型多媒体数据。第三，BDAP将实现新范式的实验，以整合来自多个来源的大型生物医学数据，包括图像、基因组、定量、生物和观察数据。第四，BDAP将能够测试生成、存储、分析和整合大型微生物组、健康和社会经济数据集的技术，以通过生物信息学和数据挖掘方法确定特定微生物图谱与人类健康之间的因果关系。最后，该项目将有助于开发新的统计方法，用于分析表观遗传学、药物基因组学和基因组关联研究的高维数据。这款内部仪器能够对PB级的敏感数据集进行能耗测量和永久存储。在推进工程学知识的同时，该仪器支持计算、统计和生物工程研究，将工程学原理应用于生物学和医学中的几个问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.