基于异构数据融合的数据库智能集成方法

Database intelligence integration method based on heterogeneous data fusion

目前研究的数据库智能集成方法存在的集成精度低、集成时间长、数据失效率高、集成效果差等问题,该文研究了一种基于异构数据融合的数据库智能集成方法。采用权重平均法、D-S证据理论法和投票表决法实现复杂的异构数据融合,对来自不同数据源的多方数据进行协同处理,减少数据库数据的冗余现象,综合互补数据信息,明确数据间的关联性,便于提取协同信息。并通过建立公共模型、明确查询处理器的查询分配,确立数据库的关系模式,便于数据库的管理,同时制定详细的数据查询流程,以快速且准确地响应用户查询需求,完善数据库的集成功能,并促进异构数据融合技术的进一步发展和应用。实验结果表明,基于异构数据融合的数据库智能集成方法能够有效提高集成精度,缩短集成时间,降低数据失效率。

The current database intelligent integration methods have some problems,such as low integration accuracy,long integration time,high data failure rate,poor integration effect,etc.This paper studies a database intelligent integration method based on heterogeneous data fusion.Three methods are adopted to realize complex heterogeneous data fusion,namely,weight average method,D-S evidence theory method and voting method.Multi party data from different data sources are processed cooperatively to reduce the redundancy of database data.Complementary data information is integrated to clarify the correlation between data and facilitate the extraction of collaborative information.And by establishing a common model,defining the query allocation of the query processor,establishing the relational model of the database to facilitate the management of the database,and at the same time formulating a detailed data query process to quickly and accurately respond to user query needs,improve the database integration,and promote further development and application of heterogeneous data fusion technology.The experimental results show that the database intelligent integrated method based on heterogeneous data fusion can effectively improve integrated accuracy,shorten the integrated time,and reduce data loss efficiency.