skyline query under multidimensional incomplete data based on classification tree

Abstract

A method for skyline query of multidimensional incomplete data based on a classification tree has been proposed to address the problem of a large amount of useless data in existing skyline queries with multi-dimensional incomplete data, which leads to low query efficiency and algorithm performance. This method consists of two main parts. The first part is the proposed incomplete data weighted classification tree algorithm. In the first part, an incomplete data weighted classification tree is proposed, and the incomplete data set is classified using this tree. The data classified in the first part serves as the basis for the second step of the query. The second part proposes a skyline query algorithm for multi-dimensional incomplete data. The concept of optimal virtual points has been recently introduced, effectively reducing the number of comparisons of a large amount of data, thereby improving the query efficiency for incomplete data. Theoretical research and experimental analysis have shown that the proposed method can perform skyline queries for multi-dimensional incomplete data well, with high query efficiency and accuracy of the algorithm.