基于不确定理论的不确定性数据Top-k查询计算

在不确定性数据集中,基于参数化排名函数的Top-k查询研究近年来备受关注。给出了一种新的解决方法,该方法将不确定性数据集中的元组建模为不确定网络,将有序元组的Top-k查询等价转化为相应样本图中边的不确定测度关系,并对样本图依据所包含边的排序位置进行分类,从而 将不确定性数据中基于参数化排名函数的Top-k查询等价转换为依Top-k值不同的有限查询。本算法避免了计算所有元组在样本图中的排名不确定测度值,提高了不确定图的Top-k查询计算效率。 理论分析和实验结果表明,提出的Top-k查询算法能够从非确定角度解决不确定性数据的Top-k查询计算问题。

Top-k Query Calculation of Uncertain Data Based on Uncertainty Theory

The Top-k query in the uncertain data set based on parametric ranking function has been focused in recent years.This paper gave out a new solution.The tuples of uncertain data set is modeled as uncertain network,Top-k query of the orderly tuples is transformed equivalently into uncertain measure relations of edges in corresponding sample fi-gures,and the sample figures are classified according the ranking position of edge contained in them.So the Top-k query in the uncertain data set based on parametric ranking function is transformed equivalently into different limited query with different Top-k value.The proposed algorithm avoids calculating the ranking uncertain measure values of all tuples in the sample figures,and improves the computation efficiency of Top-k query in uncertain figure.Theoretical analysis and experimental results show that the proposed Top-k query algorithm can solve Top-k query calculation of uncertain data from the uncertainty perspective.