Abstract: Most distributed and parallel Skyline query algorithms based on data vertical partition have poor parallelism, which makes them inadaptable to the queries on massive data with fast response requirement. This paper proposes an effective distributed and parallel Skyline query algorithm named PDS-VP(Parallel and Distributed Skyline query for Vertical Partitioning datasets). There are two kinds of nodes in PDS-VP: the coordinator and the participant. Tasks of the random access and Skyline computation in locals in the coordinator are assigned to the participants to enhance the parallelism, so as to improve the efficiency of the algorithms. Experimental results show that PDS-VP has higher parallelism and is more effective than the existing methods.
data vertical partition,