Parallel n-of-N Skyline Queries over Uncertain Data Streams

Abstract

The skyline query over uncertain data streams has attracted considerable attention recently, due to its significance in helping users analyze big data. However, existing uncertain skyline queries with sliding window model only focus on retrieving the most recent N streaming items, which limits the query flexibility and efficiency. In this paper, we propose an efficient parallel method for processing uncertain n-of-N skyline queries. Specifically, we define the parallel uncertain skyline queries with n-of-N model, and propose a novel parallel query framework. Moreover, we propose a sliding window partitioning strategy, as well as a streaming items mapping strategy to realize the load balance. Additionally, we provide an encoding interval technique to further improve the query efficiency. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our proposals.