MRI: Acquisition of Big-Data Private-Cloud Research Cyberinfrastructure (BDPC)

提案#：13-38099PI(S)：Vardi，Mohe；Allen，Genevera；Bradshaw，Stephen；Karaki，Lydia；Veeraraghavan，AshkInstitution：莱斯大学标题：MRI/Acq.：大数据私有云研究网络基础设施(BDPC)项目建议：该项目获取用于大数据云计算的新型网络基础设施工具，设计为具有大内存需求的松散耦合计算系统，支持广泛的应用领域以及对云计算基础设施的研究。涉及的领域科学范围从开发跨越基础计算机科学工作的大数据使能软件技术，到分析电子病历、推特数据流和飓风疏散战略。预计在了解疾病和治疗方法以及在机器学习、优化、压缩传感、图像处理以及统计分析和数据挖掘等领域开发和应用数学模型方面将带来更多好处。该仪器还将有助于弥合天体物理学中数字模型和观测之间的差距。更广泛的影响：对社会的更广泛影响，特别是在教育和培训方面(包括对代表不足的群体的成员)，都是令人信服的。该仪器将直接影响所有参加计算和计算问题解决课程的学生的教育体验。目标研究社区是多样和广泛的，包括代表性不足的群体，具有很强的实证和实验成分。建议的工具非常适合于培训和教育。

Proposal #: 13-38099PI(s): Vardi, Moshe; Allen, Genevera; Bradshaw, Stephen; Karaki, Lydia; Veeraraghavan, AshokInstitution: Rice UniversityTitle: MRI/Acq.: Big-Data Private Cloud Research Cyberinfrastructure (BDPC) Project Proposed:This project, acquiring a novel cyber-infrastructure instrument for big data cloud computing designed as a loosely coupled computations system with large memory requirements, enables a significant range of application domains as well as research into infrastructures for cloud computing. The domain sciences addressed range from development of big-data enabling software technologies spanning fundamental computer science work, to the analysis of electronic medical records, twitter streams, and hurricane evacuation strategies. Additional benefits are expected in understanding disease and therapeutic treatments, and in the development and application of mathematical models in the areas of machine learning, optimization, compressed sensing, image processing, and statistical analysis and data mining. The instrument will also help bridge the gap between numerical models and observations in astrophysics.Broader Impacts: The broader impacts on society, and especially in education and training (including for members of underrepresented groups) are all compelling. The instrument will directly impact the educational experience for all students taking classes in computing and computational problem solving. The targeted research communities are diverse and broad, including the underrepresented groups, with strong empirical and experimental components. The proposed instrument is highly suitable for training and education.