Probabilistic Reverse Skyline Query Processing on Uncertain Data Streams

Reverse skyline query has played an important role in making effective market decisions. Because the flow property and uncertainty of data are more and more apparent, probabilistic reverse skyline query on uncertain data streams has become a new study task. In order to solve the problem of probabilistic reverse skyline query on uncertain data streams efficiently, firstly, through analyzing practical applications’ requirements, the definition of probabilistic reverse skyline on uncertain data streams is proposed; and then according to the relevant concepts, the index model of probabilistic reverse skyline on uncertain data streams is proposed. Next, through the detailed and in-depth analysis of probabilistic reverse skyline’s properties on uncertain data streams, a novel algorithm, probabilistic reverse skyline on uncertain data streams based on R-tree index (RT2RS), is proposed. RT2RS algorithm makes use of an efficient pruning strategy to avoid a large number of invalid operations. Finally, the performance of RT2RS algorithm is verified by a large number of simulation experiments. The experimental results show that RT2RS algorithm is an effective way to solve the problem of probabilistic reverse skyline on uncertain data streams; it could significantly reduce the execution time of probabilistic reverse skyline query on uncertain data streams and meet the requirements of practical applications.