Parallel skyline queries over uncertain data streams in cloud computing environments

摘要: Skyline query processing over uncertain data streams has attracted considerable attention recently, due to its importance in helping users make intelligent decisions on complex data. Nevertheless, existing studies only focus retrieving the skylines a centralised environment typically with one processor, which limits scalability and cannot meet requirement for massive analysis. Cloud computing provides unprecedentedly opportunities supporting management, can be well adapted parallel skyline queries. In this paper, we extensively study problem cloud environments. Particularly, three models SPM, APM, DPM are proposed address based sliding window partitioning. Additionally, an adaptive granularity adjustment strategy load balance further optimise Extensive experiments conducted demonstrate effectiveness efficiency of proposals.