中文摘要：

Skyline计算是多准则决策，数据挖掘和数据库可视化的重要操作。移动对象在运动过程中，由于位置信息的不确定，导致局部各数据点间的支配关系不稳定，从而影响全局概率Skyline集合。针对分布式环境下不确定移动对象的连续概率Skyline查询更新进行研究，提出了一种降低通信开销的连续概率Skyline查询的有效算法CDPS—UMO，该算法在局部节点中对局部概率Skyline点的变化进行跟踪；提出了有效的排序方法和反馈机制，大大降低了通信开销和计算代价；提出一种基本算法naive，与CDPS—UMO进行了对比实验，实验结果证明了算法的有效性。

英文摘要：

Skyline computation has played a significant role in the fields of multi-criteria decision making, data mining and data- base visualization. The uncertainty of moving objects makes the dominant relationship of data instable, which will affect global probabilistic skyline set. In this paper, the updating of continuous probabilistic Skyline queries is studied, which is under distrib- uted environment with the uncertainty of moving objects. A continuous probabilistic Skyline queries algorithm in order to reduce communication cost called CDPS-UMO is proposed. The change of local probabilistic Skyline points in local sites is traced. The SM （Sort Method） is introduced, and the feedback rules are proposed, which will reduce the correspondence and computation cost. A base algorithm naive is proposed to be compared with CDPS-UMO. The experiments have positive results that show effec- tiveness of the proposed algorithm.