Indexing Uncertain Data for Supporting Range Queries

Probabilistic range query is a typical and a fundamental problem in probabilistic DBMS. Although the existing solutions provide a good performance, there are some shortages that are needed to be overcomed. In this paper, we firstly propose a novel structure called MRST to approximately capture the probability density function of uncertain object. Through considering the gradient of the probability density function, MRST could provide uncertain object with strong pruning power and consume fewer space cost. Based on characters of MRST, we also design an efficient algorithm to access MRST. We propose a novel index named R-MRST to efficiently support range query on multidimensional uncertain data. Its has a strong pruning power. At the same time, it has a lower cost both in space and dynamic update. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.