移动计算环境下远程用户体验数据挖掘方法研究

在移动计算环境下,通过对远程用户的体验数据优化挖掘,满足远程用户的个性化需求,提高对远程用户QoS服务质量。传统的数据挖掘方法采用显著特征关联信息提取算法,当远程用户体验数据之间的差异性特征不明显时,挖掘的准确性不好。提出一种基于关联用户自适应链路跟踪补偿的移动计算环境下远程用户体验数据挖掘模型,进行远程用户体验数据挖掘模型的总体设计和数据结构特征分析,对采集的远程用户体验数据进行非线性时间序列分解,对数据序列通过自相关特征匹配和特征压缩实现挖掘数据的指向性信息优化提取,采用关联用户自适应链路跟踪补偿方法实现对数据挖掘误差的控制和补偿,提高了数据挖掘的准确性和有效性。仿真结果表明,采用该挖掘方法进行移动计算环境下远程用户体验数据挖掘的准确度高,实时性较好,满足了移动远程用户的个性化需求,提高了对用户服务的针对性。     

非交换环的拟零因子图

In this paper, a new class of rings, called FIC rings, is introduced for studying quasi-zero-divisor graphs of rings. Let R be a ring. The quasi-zero-divisor graph of R, denoted by Γ_*(R), is a directed graph defined on its nonzero quasi-zero-divisors, where there is an arc from a vertex x to another vertex y if and only if x Ry = 0. We show that the following three conditions on an FIC ring R are equivalent:(1) χ(R) is finite;(2) ω(R) is finite;(3)Nil_*R is finite where Nil_*R equals the finite intersection of prime ideals. Furthermore, we also completely determine the connectedness, the diameter and the girth of Γ_*(R).