Parallel skyline queries over uncertain data streams in cloud computing environments

Abstract

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 on retrieving the skylines over data streams in a centralised environment typically with one processor, which limits the scalability and cannot meet the requirement for massive data analysis. Cloud computing provides unprecedentedly opportunities for supporting massive data management, which can be well adapted to the parallel skyline queries. In this paper, we extensively study the parallel skyline query problem over uncertain data streams in cloud computing environments. Particularly, three parallel models SPM, APM, and DPM are proposed to address the problem based on the sliding window partitioning. Additionally, an adaptive sliding granularity adjustment strategy and a load balance strategy are proposed to further optimise the queries. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposals.