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: 1218808
• 负责人: Chandra Krintz
• 金额: $ 24.69万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218808&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，应用程序已经变得以数据为中心，数据产品的数量和大小都呈爆炸性增长。 虽然使用分析和处理所需的计算能力访问这些数据是便宜的，并且可以通过云计算（对巨大资源池的直观实用型访问）轻松获得，但这样做目前需要大量的专业知识，经验和时间（用于定制，配置，部署等）。 这项工作调查云计算的新模式，结合联合收割机领域为目标的语言与可扩展的数据处理，共享和管理抽象的分布式服务平台，“规模”程序员的生产力。 为了实现这一点，本研究探索了新的编程语言，运行时和分布式系统技术，这些技术将R编程语言环境与开源云平台即服务（PaaS）集成在一起，简化了处理大量数据集，跨应用程序和用户共享数据集，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更广泛的用户更容易访问云计算：使用R统计分析环境将其技术和算法应用于生物学、化学、物理学、政治学和金融等领域的重要问题的数据分析社区，使他们能够透明地使用云资源进行分析，并以使其他人能够复制和验证的方式共享他们的科学数据/结果。