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

随着计算和通信资源成本的直线下降，应用程序变得以数据为中心，数据产品在数量和大小上都呈爆炸性增长。尽管使用分析和处理所需的计算能力访问此类数据很便宜，而且可以通过云计算随时获得(直观、实用的方式访问大量资源池)，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，该模型将面向领域的语言与分布式服务平台中的可伸缩数据处理、共享和管理抽象结合在一起，以“扩展”程序员的工作效率。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和技术，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，从而简化了处理海量数据集、跨应用程序和用户共享数据集以及跟踪和执行数据来源的方式。PIS的研究、推广、综合课程和研究成果开源发布计划有可能使云计算更容易被更广泛的用户访问：数据分析社区，他们使用R统计分析环境将他们的技术和算法应用于生物、化学、物理、政治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。

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.