Abstract
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.
The work is partially supported by the National Basic Research Program of China (973 Program) (No. 2012CB316201,2011CB302200-G), the National Natural Science Foundation of China (Nos. 61322208, 61272178, 61129002), the Doctoral Fund of Ministry of Education of China (No. 20110042110028), and National High Technology Research and Development 863 Program of China (GrantNo.2012AA011004).
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Zhu, R., Wang, B., Wang, G. (2014). Indexing Uncertain Data for Supporting Range Queries. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_10
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DOI: https://doi.org/10.1007/978-3-319-08010-9_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08009-3
Online ISBN: 978-3-319-08010-9
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