Probabilistic top-k dominating queries in uncertain databases

Abstract

Due to the existence of uncertain data in a wide spectrum of real applications, uncertain query processing has become increasingly important, which dramatically differs from handling certain data in a traditional database. In this paper, we formulate and tackle an important query, namely probabilistic top-k dominating (PTD) query, in the uncertain database. In particular, a PTD query retrieves k uncertain objects that are expected to dynamically dominate the largest number of uncertain objects. We propose an effective pruning approach to reduce the PTD search space, and present an efficient query procedure to answer PTD queries. Moreover, approximate PTD query processing and the case where the PTD query is issued from an uncertain query object are also discussed. Furthermore, we propose an important query type, that is, the PTD query in arbitrary subspaces (namely SUB-PTD), which is more challenging, and provide an effective pruning method to facilitate the SUB-PTD query processing. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed PTD query processing approaches.