Probabilistic Reverse Skyline Query Processing over Uncertain Data Stream

Abstract

Reverse skyline plays an important role in market decision-making, environmental monitoring and market analysis. Now the flow property and uncertainty of data are more and more apparent, probabilistic reverse skyline query over uncertain data stream has become a new research topic. Firstly, a novel pruning technique is proposed to reduce the number of uncertain tuples reserved for processing continuous probabilistic reverse skyline query. Then some probability pruning techniques are proposed to reduce some redundant calculations. Next, an efficient algorithm, called Optimization Probabilistic Reverse Skyline (OPRS), is proposed to process continuous probabilistic reverse skyline queries. Finally, the performance of OPRS is verified through a large number of simulation experiments. The experimental results show that OPRS is an effective way to solve the problem of continuous probabilistic reverse skyline, and it could significantly reduce the executionx time of continuous probabilistic reverse skyline queries and meet the requirements of practical applications.