Establishing Dynamic Cost Models for Multidatabase Systems

多数据库系统中全局查询优化的一个关键挑战是，由于本地自治，一些所需的本地信息（例如本地成本模型）可能无法在全局级别获得。最近的文献中提出了许多应对这一挑战的技术。然而，它们仅适用于静态系统环境。本研究项目探索为多数据库系统建立动态成本模型的理论和方法。先前关于在静态系统环境中建立成本模型的查询采样方法的工作被扩展到动态系统环境。该项目采用的方法包括定性方法和自适应方法，其中定性方法将定性变量引入成本模型，自适应方法将从执行用户查询中观察到的动态成本信息自适应地合并到成本模型中。对所有方法进行评估和比较，以确定最有希望为多数据库系统建立动态成本模型的方法。还进行了利用动态成本模型的查询优化的理论和实验研究。该项目将为高效多数据库系统的设计和开发提供理论、算法和实现技术。

A crucial challenge for global query optimization in a multidatabase system is that some required local information such as local cost models may not be available at the global level due to local autonomy. A number of techniques to tackle this challenge have been suggested in the literature recently. However, they are suitable only for a static system environment. This research project explores theory and methods to establish dynamic cost models for a multidatabase system. Previous work on a query sampling method for establishing cost models in a static system environment is extended to a dynamic system environment. The methodologies adopted in this project include a qualitative approach, which introduces qualitative variables into cost models, and an adaptive approach, which adaptively incorporates dynamic cost information observed from execution of user queries into cost models. All approaches are evaluated and compared in order to determine the most promising one to establish dynamic cost models for multidatabase systems. Theoretical and experimental studies on query optimization that takes advantage of dynamic cost models are also conducted. The project will provide theory, algorithms, and implementation techniques for the design and development of an efficient multidatabase system.