CAREER: Interactive and Collaborative Data Management in the Cloud

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。科学数据管理领域正在发生变化。仪器和模拟软件的改进使科学家能够以前所未有的规模访问数据。这些数据越来越多地存储在运行数千台商用服务器的数据中心中。这种新环境带来了重大的数据管理挑战。除了高效的查询处理之外，庞大的数据和查询还需要新的查询管理技术，如运行时查询控制、查询内部容错、查询组合支持和无缝查询共享。这个项目正在开发一系列技术来支持上述查询管理任务。为了实现这一目标，该项目包括设计和实现一个原型大规模并行数据库管理系统，该系统可作为开发各种查询管理方案的平台。在科学领域的合成数据和实际数据上对新算法进行了评估。该项目的预期结果包括各种运行时查询管理算法，包括并行查询进度指示器、分布式查询内容错以及根据需要暂停和恢复查询的能力。预期的结果还包括用于搜索先前执行的查询、注释查询和与他人共享查询的工具。总之，这些工具有望显著改善大规模的数据分析，使其成为一个互动和协作的过程。通过上述贡献，该项目将对目前受数据分析能力限制的科学界产生重大影响。本项目产生的软件和技术文件将通过项目网站（http://nuage.cs.washington.edu/）发布。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The scientific data management landscape is changing. Improvements in instrumentation and simulation software are giving scientists access to data at an unprecedented scale. This data is increasingly being stored in data centers running thousands of commodity servers. This new environment creates significant data management challenges. In addition to efficient query processing, the magnitude of data and queries call for new query management techniques such as runtime query control, intra-query fault tolerance, query composition support, and seamless query sharing.This project is developing a series of techniques to support the above query management tasks. To achieve this goal, the project includes the design and implementation of a prototype massively parallel database management system that serves as the platform for the development of various query management schemes. The new algorithms are evaluated on both synthetic and real data from the scientific domain.The expected results of this project include a variety of runtime query management algorithms including parallel query progress indicators, distributed intra-query fault-tolerance, and the ability to suspend and resume queries as needed. The expected results also include tools for searching previously executed queries, annotating them, and sharing them with others. Together, these tools hold the promise to significantly improve data analysis at massive scale, making it an interactive and collaborative process.Through the above contributions, this project will have significant impact on the scientific community, currently limited by their ability to analyze data. The software and technical papers resulting from this project will be disseminated through the project website (http://nuage.cs.washington.edu/).