关键词: MapReduce, Hadoop, 概率Skyline, 不确定移动对象

Abstract: As big data has been a challenge to traditional Skyline research, computing Skyline using parallel framework of MapReduce is now a research hotspot. This paper studies a Skyline query of an uncertain moving object and proposes a continuous probabilistic Skyline query algorithm based on event tracking, named MR-DTrack (domination-track algorithm based on MapReduce). Firstly, partitioning method based on angular is adopted to make workload balance, a pre-computation is used to get the time when the Skyline sets change possibly, and the candidate probabilistic Skyline sets can be got in the Reduce stage. Then local filter points are used to prune in order to reduce computing costs. Finally, the global probabilistic Skyline set is computed by combining the candidate skyline sets. Experiments over artificial and real data sets prove the efficiency and effective of the new algorithm.

Key words: MapReduce, Hadoop, probabilistic Skyline, uncertain moving object