Establishing Dynamic Cost Models for Multidatabase Systems

在多数据库系统中，全局查询优化的一个关键挑战是，一些所需的本地信息，如本地成本模型可能无法在全球范围内，由于局部自治。最近文献中提出了一些应对这一挑战的技术。但是，它们仅适用于静态系统环境。本研究探讨建立多资料库系统动态成本模型的理论与方法。将以往在静态系统环境下建立代价模型的查询抽样方法扩展到动态系统环境。在这个项目中采用的方法包括一个定性的方法，它引入了定性变量的成本模型，和自适应的方法，自适应地将动态的成本信息，从执行用户查询到成本模型。所有的方法进行评估和比较，以确定最有前途的一个建立多数据库系统的动态成本模型。本文还对利用动态代价模型进行查询优化的方法进行了理论和实验研究。该项目将提供理论，算法和实现技术的设计和开发一个有效的多数据库系统。

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.