CAREER: Techniques and Applications of Derived Data Maintenance

本研究项目的目标是开发有效的方法来维护数据库系统中的派生数据。该方法结合了传统上针对不同形式的派生数据(例如，缓存、索引、副本等)单独开发的想法。开发新的派生数据维护技术，包括(1)缓存辅助数据以改进数据库视图维护；(2)利用更新流的特征优化更新处理；(3)具有有界误差的近似视图维护。该研究具有广泛的应用，如数据仓库、广域复制和动态数据缓存。具体地说，该项目的成果将用于开发生物医学研究的综合数据空间和建立可扩展的网络服务的数据库复制工具，并产生超出数据库领域的实际影响。这对整个社会产生了更广泛的影响。有关该项目的更多信息，请访问网站http://www.cs.duke.edu/dbgroup/ddm/.该项目的教育部分侧重于更新数据库课程，在研究生和本科生一级开设三门新课程，将当前的研究主题纳入教学，吸引具有不同学术和文化背景的学生，并帮助他们将数据库技能应用于其研究领域或职业生涯。

The goal of this research project is to develop efficient methods for maintaining derived data in database systems. The approach combines ideas that have been traditionally developed separately for different forms of derived data (e.g., caches, indexes, replicas, etc.) to develop new techniques for derived data maintenance, including (1) caching auxiliary data to improve database view maintenance, (2) optimizing update processing using characteristics of update streams, and (3) approximate view maintenance with bounded error. The research has a broad range of applications such as data warehousing, wide-area replication, and caching of dynamic data. Specifically, the results of this project will be applied to the development of an integrated dataspace for biomedical research and database replication tools for building scalable Web services, and generate practical impacts beyond the database field. This has broader impact on society in general. Further information on this project can be found on the web site http://www.cs.duke.edu/dbgroup/ddm/. The educational component of this project focuses on updating the database curriculum with three new courses at graduate and undergraduate levels, incorporating current research topics into teaching, attracting students with diverse academic and cultural backgrounds and helping them apply database skills to their fields of research or in their careers.