III: Medium: Collaborative Research: Database-As-A-Service for Long Tail Science

随着科学应用中存在大量数据，数据库管理成为一个关键问题，但数据库技术却没有跟上。这个问题在科学的长尾中尤其严重：大量相对较小的实验室和个体研究人员共同产生了大多数科学成果。这些研究人员缺乏IT人员和专业技能来大规模部署技术，但已经开始例行访问数百个文件和潜在的tb级数据来回答一个科学问题。本项目开发了一个科学数据库即服务平台的体系结构。它探索了自动化其余使用障碍的技术：从本地源摄取数据并自动引导一组初始查询和可视化，部分是通过积极挖掘数据、查询和用户活动的共享语料库。它研究了提取全球知识和模式的方法，同时为科学家提供对数据的访问控制，以及一些正式的隐私保证。该建议的智力优势包括自动化与数据工作相关的重要认知任务：从非结构化数据源提取信息、数据清理、逻辑模式设计、隐私控制、可视化和应用程序构建。作为更广泛的影响，该项目帮助科学家减少了“处理数据”而不是“做科学”的时间比例。这个项目产生的所有软件都是开源的，所有的发现都通过出版物和研讨会广泛传播。对该软件的科学用户的可持续支持是通过华盛顿大学科学研究所进行协调的。该研究被纳入了本科和研究生的计算机科学课程，该软件也被纳入了领域科学课程。该项目的外展活动包括通过针对代表性不足的群体（如CRA-W DREU）的特殊方案为学生提供建议。关于这个项目的更多信息可以在http://escience.washington.edu/dbaas上找到。

With tremendous amounts of data existing in scientific applications, database management becomes a critical issue, but database technology is not keeping pace. This problem is especially acute in the long tail of science: the large number of relatively small labs and individual researchers who collectively produce the majority of scientific results. These researchers lack the IT staff and specialized skills to deploy technology at scale, but have begun to routinely access hundreds of files and potentially terabytes of data to answer a scientific question. This project develops the architecture for a database-as-a-service platform for science. It explores techniques to automate the remaining barriers to use: ingesting data from native sources and automatically bootstrapping an initial set of queries and visualizations, in part by aggressively mining a shared corpus of data, queries, and user activity. It investigates methods to extract global knowledge and patterns while offering scientists access control over their data, and some formal privacy guarantees. The Intellectual Merit of this proposal consists of automating non-trivial cognitive tasks associated with data work: information extraction from unstructured data sources, data cleaning, logical schema design, privacy control, visualization, and application-building. As Broader Impacts, the project helps scientists reduce the proportion of time spent "handling data" rather than "doing science." All software resulting from this project are open source, and all findings are disseminated broadly through publications and workshops. Sustainable support for science users of the software is coordinated through the University of Washington eScience Institute. The research is incorporated in both undergraduate and graduate computer science courses, and the software is also incorporated into domain science courses as well. The project's outreach activities include advising students through special programs geared toward under-represented groups such as the CRA-W DREU. More information about this project is found at http://escience.washington.edu/dbaas.