Expert Systems for Database Query Optimization

方法时，程序中嵌入的数据库查询将被优化 程序已编译。执行嵌入式数据库查询 重复地使用不同的变量实例化，即不同的 查询谓词中的常量。 为了优化一个 嵌入式查询时，查询优化器必须假设 中显示为常量的程序变量的值 查询和关于存储在数据库中的数据，以及 结果查询计算计划的最优性取决于 这些假设的有效性。 本建议旨在制定标准， 需要重新优化，如何实施这些标准 以及如何避免重新优化。通过使用 一种叫做动态查询评估计划的新技术。 查询 优化器将确定哪些情况值得区分， 使用多目标准备多个查询评估计划 搜索策略，并为运行时准备决策树 系统 新的查询优化技术承诺 传统和高级数据模型的优点。

A database query embedded in a program is optimized when the program is compiled. An embedded database query is executed repeatedly with different variable instantiations, i.e. different constants in the query predicate. In order to optimize an embedded query, the query optimizer must make assumptions about the values of the program variables that appear as constants in the query and about the data stored in the database, and the optimality of the resulting query evaluation plan depends on the validity of these assumptions. This proposal aims at developing criteria when reoptimization is required, how these criteria can be implemented efficiently, and how reoptimization can be avoided. by using a new technique called dynamic query evaluation plans. The query optimizer will determine which cases are worth distinguishing, prepare more than one query evaluation plan using a multi-goal search strategy, and prepare a decision tree for the run-time system. The new query optimization technology promises advantages for both conventional and advanced data models.