CAREER: Scrapple: Fast Analytical Query Evaluation via Advanced Query Recycling Techniques

决策支持应用程序的复杂的分析查询的特点是可以是非常昂贵的计算，这样的应用程序的价值是直接相关的速度，答案可以返回给用户。 通常，一旦查询得到回答，数据库系统就会简单地丢弃结果。 然而，这样做会错过一个巨大的优化机会：如果我们只知道如何回收它们来帮助回答后续的相关查询，那么丢弃的查询结果中会有巨大的潜在能量。 该项目的目标是开发Scrapple，这是一个有原则的数据库管理系统，它积极地重用旧的查询结果，以加快对新查询的回答，从而为一大类决策支持应用程序带来潜在的巨大性能提升。（以及它们的中间子结果）作为物化视图，然后采用高级技术来优化查询，使用物化视图来回答后续查询。 为了执行这一策略，该项目开发：（1）一个新的和全面的理论差分重构策略;（2）一套统一的原则连接增量视图维护和优化查询使用物化视图;（3）一个新的和全面的理论聚合查询的数据出处;和（4）实际的实现技术，通过基于成本的搜索策略回收缓存的结果。 通过使用完全自动化的技术，Scrapple将大大降低典型数据仓库的总拥有成本。 此外，在我们的方法的核心技术有广泛的应用领域，如数据集成，数据交换，视图维护和数据出处。 这项研究还将用于为新的课程模块编写讲座和项目材料。这些教育材料、沿着Scrapple源代码和出版物将在项目网站www.example.com上免费提供。

The complex analytical queries characterizing decision support applications can be very expensive to compute, and the value of such applications is directly correlated to the speed at which answers can be returned to the user. Typically, once queries have been answered, database systems simply discard the results. However, a huge optimization opportunity is missed by doing this: there is tremendous latent energy in the discarded query results, if we only knew how to recycle them to help answer subsequent related queries. The goal of the project is to develop Scrapple, a principled database management system that aggressively reuses old query results to speed up the answering of new queries, resulting in potentially dramatic performance gains for a large class of decision support applications.Scrapple's basic strategy is to view cached query results (and their intermediate subresults) as materialized views, and then employ advanced techniques for optimizing queries using materialized views to answer subsequent queries. To execute this strategy, the project develops: (1) a novel and comprehensive theory of differential reformulation strategies; (2) a set of unifying principles connecting incremental view maintenance and optimization of queries using materialized views; (3) a novel and comprehensive theory of data provenance for aggregate queries; and (4) practical implementation techniques for recycling cached results via cost-based search strategies. By using fully automated techniques, Scrapple will dramatically reduce the total cost of ownership of a typical data warehouse. Moreover, the techniques at the heart of our approach have wide application in areas such as data integration, data exchange, view maintenance, and data provenance. The research will also be used to develop lecture and project materials for new course modules. These educational materials, along the Scrapple source code and publications, will be made freely available at the project Web site, http://www.cs.ucdavis.edu/~green/scrapple.