Continuous Probabilistic Skyline Queries for Moving Objects with Uncertainty Based on Event
基于事件的位置不确定移动对象连续概率Skyline查询

Skyline queries are an important operator of location based service (LBS), which aim to find all data that are not dominated by any others. The uncertainty of moving objects makes the dominant relationship of data instable, which will affect skyline operator. In this paper, skyline inquires for moving objects with uncertainty are studied. Firstly, the dominant probability between two moving objects is defined. Then it is proposed how to compute the dominant probability and skyline probability by differential element method. A novel effective algorithm U_CPSC is presented to handle continuous probabilistic skyline queries for uncertain moving objects based on these definitions. The initial p-Skyline set is firstly searched by rapid computing. Secondly, two types of events affecting p-Skyline are defined to track and update p-Skyline set continuously instead of re-computing the whole dataset each time. A static algorithm U_SPSC is proposed to compare with U_CPSC. Experiments have positive results that show effectiveness of the proposed algorithm.