采用数据降维技术实现网络异构数据库分级数据共享

各主题数据库结构的复杂性，以及共享数据指标变量之间广泛的相关性，是异构数据库实现共享的重要障碍。目前的许多研究是针对异构数据库结构复杂问题，提出实现一致性访问的策略，但这些解决方案无法使系统达到良好的可扩展性。文中提出了一种新的解决方案，即发掘各主题数据库的结构共性，通过数据降维降低数据库结构上的复杂性，以实现库之间方便灵活的数据共享，从而达到较好的可扩展性。

IMPLEMENT DATA SHARING OVER NETWORK HETEROGENEOUS DATABASES BY DATA DIMENSION REDUCTION METHOD

The variety of databases structures as well as multiple data correlation on database index sets are major barriers on data sharing between heterogeneous databases. Most research work currently centralized on coherence access methods over heterogeneous databases, however, those methods can't establish a system in good scalability. In this paper, a new resolution is presented, which firstly find out general characteristics between many heterogeneous databases, and then the complexity of each database is reduced by using dimension reduction methods, so that data|sharing becomes more flexible with a good scalability. In addition, the principal component views(PCV) reduce data correlation between shared|databases.