Continuous probabilistic reverse skyline monitoring over uncertain data streams

Abstract

Reverse skyline is useful for supporting many applications, such as marketing decision, environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a need for processing probabilistic reverse skyline queries. In this paper, we study the problem of efficiently processing these queries on uncertain data streams. We first show the formal definitions of reverse skyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRS to maintain the most recent N uncertain data elements and to process continuous queries on them. CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structure named Characteristic Rectangle, are incorporated into it to handling the extra computing complexity arising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniques are very efficient in handling uncertain data streams.