支持Web深层数据库网格的部分关键技术的研究

当前,深层Web数据库数量急剧增加,然而其知识并没有得到有效的利用.本文提出将特定的深层Web数据库应用于网格环境中的思想,并针对支持深层Web数据库网格的部分关键技术进行研究,主要包括：（1）深层Web数据库元信息定义模型与模式抽取模型的研究;（2）多层次的模式匹配模型和自协调模型研究;（3）基于属性松弛的Web数据库查询与集成模型研究;（4）多目标函数代价模型和面向局部性的自适应优化调度模型研究.研发成果将为构建深层Web数据库网格提供良好的支持,就像网格的概念所定义的一样,为用户提供统一的接口,可按需为消费者提供集成的深层Web数据知识.其具有广阔的应用前景.

Study on Some Key Technologies Supporting Deep Web Database Grid

Nowadays,the number of deep Web databases is increasing rapidly,while their knowledge cannot be utilized effectively.This paper provides the idea of applying deep Web databases to grid environment,and focuses on some key technologies supporting deep Web database grid,mainly including:(1)Study on meta information model definition of deep Web databases and schema extraction.(2)Study on multi-level schema matching model and self-adaptive model.(3)Study on Web database query and integration model based on attributes relaxes.(4)Study on multi-object cost function model and local-oriented adaptive optimization schedule model.The research results of the project are aiming to give better support for building deep Web database grid system,just as the definition of grid,and to provide integrated deep Web data knowledge to users by means of uniform portal on demands.These key technologies will play an important role in future applications.