关键词: 曼哈顿距离, 移动计算, 概率Skyline查询, 不确定数据

Abstract: In many applications, due to the measurement instrument accuracy, update delay, network bandwidth and other restrictions, different forms of data uncertainty is widespread. At present, the information query in the uncertain data has been paid attention to by researchers in the field of database research, and it is also a hot topic to find efficient analysis method for uncertain data. This paper focuses on the uncertain moving object probability Skyline query problem based on Manhattan distance, and proposes a probability Skyline model based on Manhattan distance. Skyline model is used to solve the uncertainty of moving object at some point and obtain a p-t-Skyline result set. The result set contains all the moving objects whose Skyline probability is at least p at time t. In practical applications, the Skyline probability process for calculating a large number of uncertain moving objects is cumbersome and costly. In order to improve the computational efficiency of the probability Skyline query process, this paper presents a solution that includes four steps: sampling, bounding, pruning and refining. At the same time, a multidimensional index structure VCI tree is utilized to speed up the efficiency of data retrieval in order to further reduce Skyline computational overhead. The experimental results show that the solution has high efficiency on data sets of different scales and dimensions.

Key words: Manhattan distance, mobile computing, probabilistic Skyline query, uncertain data