MRI: Acquisition of Big Data Training and Research Laboratory

该项目通过采购和获取适合大数据研究的大型计算集群，旨在实现生物信息学、海洋能源、社交媒体挖掘、环境和气候建模、图像处理和分析应急响应、健康和医疗信息学、国家安全、基础设施维护和可靠性、执法、商业和制造业等国家关注的多个领域的研究。特别值得注意的是跨越广泛应用领域的七个项目。这包括——扩展LexisNexis的高性能计算集群（HPCC）平台，以纳入更广泛的算法；-生物信息学大数据分析；-使用机器状态监测和预测健康监测来提高海洋涡轮机的可靠性；-挖掘流数据时发现的一般挑战；-分析水声信号，以执行探测未爆弹药等任务；-使用机器学习改进视频压缩方案；和-气候模拟的挑战。所有这些都需要先进的计算资源。大数据的研究包括对超大数据集的分析，建立能够包含大量实例和特征的模型，以便做出可靠的预测和联系。维护和促进大数据的发展已经成为一项必不可少的活动，以确保今天看来不可逾越的问题明天可能得到解决。这种增长的两个方面是为学生开发和提供大数据研究所需的工具和技术的课程，以及为现有的研究人员提供更有针对性的这些工具培训，这些研究人员的专业领域在于其他方面的研究。将遵循文化多样性的最佳做法，包括来自代表性不足群体的学生和研究人员。

This project, procuring and acquiring a large computing cluster appropriate for Big Data research, aims to enable research in a number of fields of national concern such as bioinformatics, ocean energy, social media mining, environmental and climate modeling, image processing and analysis emergency response, health and medical informatics, national security, infrastructure maintenance and reliability, law enforcement, commerce, and manufacturing. Of particular note are seven projects spanning a wide range of application domains. These include the- Extension of LexisNexis's High Performance Computing Cluster (HPCC) platform to incorporate a Wider Range of Algorithms;- Analysis of Big Data for Bioinformatics;- Use of Machine Condition Monitoring and Prognostic Health Monitoring to Improve Ocean Turbine Reliability;- General Challenges found when Mining Streaming data;- Analysis of Underwater Acoustic Signals for Tasks such as Unexploded Ordinance Detection; - Use of Machine Learning to Improve Video Compression Schemes; and- Challenges of Climate Modeling.All these demand the advanced computation resources under this acquisition. The study of Big Data encompasses the analysis of extremely large datasets, building models which are able to incorporate vast numbers of instances and features in order to make reliable predictions and connections.Maintaining and promoting the growth of Big Data has become an essential activity to ensure that the problems that now seem insurmountable may be solved tomorrow. Two aspects of this growth are developing and providing courses for students focused on the tools and techniques necessary for Big Data research, and more focused training in these tools for existing researchers whose area of expertise lies in other aspects of research. Best practices will be followed for cultural diversity, involving students and researchers from underrepresented groups.