Top k probabilistic skyline queries on uncertain data

Abstract Uncertainty of data is inherent in many applications, and query processing over uncertain data has gained widespread attention. The probabilistic skyline query is a powerful tool for managing uncertain data. However, the famous probabilistic skyline query, called p-skyline query, is likely to return unattractive objects which have no advantage in either their attributes or skyline probabilities with comparing to other query results. Moreover, it may return too many objects to offer any meaningful insight for customers. In this paper, we first propose a modified p-skyline (MPS) query based on a strong dominance operator to identify truly attractive results. Then we formulate a top k MPS (TkMPS) query on the basis of a new ranking criterion. We present effective approaches for processing the MPS query, and extend these approaches to process the TkMPS query. To improve the query performance, the reuse technique is adopted. Extensive experiments verify that the proposed algorithms for the MPS and TkMPS queries are efficient and effective, our MPS query can filter out 34.44% unattractive objects from the p-skyline query results at most, and although in some cases the results of the MPS and the p-skyline queries are just the same, our MPS query needs much less CPU, I/O, and memory costs.