CSR: CC: Small: Collaborative Research: Language and Runtime Support for Large-Scale Data Analytics

CSR：CC：小型：协作研究：大规模数据分析的语言和运行时支持

• 批准号: 1460683
• 负责人: Jan Vitek
• 金额: $ 13.7万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1460683&HistoricalAwards=false
• 关键词: CSR; CC; Small; Collaborative; Research

As the cost of computing and communication resources has plummeted, applications have become data-centric with data products growing explosively in both number and size. Although accessing such data using the compute power necessary for its analysis and processing is cheap and readily available via cloud computing (intuitive, utility-style access to vast resource pools), doing so currently requires significant expertise, experience, and time (for customization, configuration, deployment, etc). This work investigates new models of cloud computing that combine domain-targeted languages with scalable data processing, sharing, and management abstractions within a distributed service platform that "scales" programmer productivity. To enable this, this research explores new programming language, runtime, and distributed systems techniques and technologies that integrate the R programming language environment with open source cloud platform-as-a-service (PaaS) in ways that simplify processing massive datasets, sharing datasets across applications and users, and tracking and enforcing data provenance. The PIs' plans for research, outreach, integrated curricula, and open source release of research artifacts have the potential for making cloud computing more accessible to a much wider range of users: The data analytics community who use the R statistical analysis environment to apply their techniques and algorithms to important problems in areas such as biology, chemistry, physics, political science and finance, by enabling them to use cloud resources transparently for their analyses, and to share their scientific data/results in a way that enables others to reproduce and verify them.

随着计算和通信资源成本的大幅下降，应用程序已变得以数据为中心，数据产品的数量和规模都呈爆炸式增长。 尽管使用分析和处理所需的计算能力来访问此类数据很便宜，并且可以通过云计算轻松获得（对庞大资源池的直观、实用式访问），但目前这样做需要大量的专业知识、经验和时间（用于定制、配置、部署等）。 这项工作研究了云计算的新模型，该模型将特定领域的语言与可扩展的数据处理、共享和管理抽象相结合，并在分布式服务平台中“扩展”程序员的生产力。 为了实现这一目标，本研究探索了新的编程语言、运行时和分布式系统技术，将 R 编程语言环境与开源云平台即服务 (PaaS) 集成，以简化处理海量数据集、共享数据集的方式跨应用程序和用户，以及跟踪和执行数据来源。 PI 的研究、推广、综合课程和研究成果的开源发布计划有可能使云计算更容易被更广泛的用户使用：使用 R 统计分析环境来应用他们的数据分析社区。技术和算法解决生物学、化学、物理、政治学和金融等领域的重要问题，使他们能够透明地使用云资源进行分析，并以允许其他人复制和共享其科学数据/结果的方式验证它们。