Abstract: Skyline query is a typical multi-objective optimization problem and is widely applied in multi-objective optimization,data mining and other fields.Most of the existing Skyline query processing algorithms assume that the data set is placed in a single server,and query processing algorithm is designed as a serial algorithm for a single server.With the rapid growth of data,especially under the background of big data,the traditional serial Skyline algorithms based on a single computer are far from enough to meet the needs of users.Based on the popular distributed parallel programming framework MapReduce,this paper studied the parallel skyline query algorithm suitable for large data sets.Aiming at the factors affecting MapReduce,this paper improved the existing data partition strategy based on angles and proposed the data partition strategy based on Balanced Angular.Meanwhile,to reduce the computation of Reduce phase,this paper proposed the data filtering strategy in advance at Map.The experimental results show that the proposed Skyline query algorithm can improve system performance significantly.

Key words: MapReduce,Skyline,Data partition