基于模糊关联空间的数据过滤方法

该文提出了一种适用于大规模分布式虚拟环境的新的数据过滤方法，以解决传统过滤方法由于基于区域划分、采用组播技术而造成的效率低、稳定性差的问题。该文提出了实体关联度的概念，并在分布式虚拟环境中建立模糊关联空间，把数据过滤问题转化为在模糊关联空间中求取关联实体集的问题；最后通过实验数据和理论分析，证明基于模糊关联空间的数据过滤方法可以有效地解决传统过滤技术中存在的一系列问题，提高数据过滤的效率和稳定性。

An Approach to Data Filtering Based on Fuzzy Correlation Space

This paper presents a new approach to data filtering applied in large scale virtual environment. The structure of the paper is as follows: First, the drawbacks of traditional data filtering methods based on region dividing and multicast are analysed.There are many approaches in solving this problem (i.e. handling unclear information), such as AI and NN. In this paper, an approach based on fuzzy mathematics is used. It is proved that this approach is both simple and effective as well. With the establishment of fuzzy correlation space in VE based on the concept of entity correlation, the problem of data filtering is transformed into the problem of seeking correlative entity set in VE. Three data filtering algorithms are given in this paper, and the algorithm is used with self reliant threshold for the purpose of robustness. Finally, it is pointed out through simulation and theoretical analysis that this new approach can solve a series of problems that face traditional techniques, and improve both efficiency and robustness of data filtering in VE.