S-MRST: a novel framework for indexing uncertain data

S-MRST: a novel framework for indexing uncertain data
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S-MRST:一种用于索引不确定数据的新颖框架

DOI:
10.1007/s11280-016-0409-x
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发表时间:
2017-07
影响因子:
3.7
通讯作者:
Wang Guoren
Wang Guoren
中科院分区:
计算机科学3区
文献类型:
--
作者:
Zhu Rui;Wang Bin;Luo Shiying;Yang Xiaochun;Wang Guoren

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本文研究不确定数据的概率范围查询问题。尽管现有的解决方案可以支持此类查询,但仍有改进的空间。在本文中,我们首先提出了一种称为 S-MRST 的新型索引,用于索引不确定数据。一方面,通过使用不规则的形状来限制不确定的数据,它具有更强的空间剪枝能力。另一方面,考虑到概率密度函数的梯度,S-MRST在概率剪枝能力方面也很强大。更重要的是,S-MRST是一个通用索引,可以支持多种类型的概率查询。理论分析和广泛的实验结果证明了所提出的指标的有效性和效率。
This paper studies the problem of probabilistic range query over uncertain data. Although existing solutions could support such query, it still has space for improvement. In this paper, we firstly propose a novel index called S-MRST for indexing uncertain data. For one thing, via using an irregular shape for bounding uncertain data, it has a stronger space pruning ability. For another, by taking the gradient of probability density function into consideration, S-MRST is also powerful in terms of probability pruning ability. More important, S-MRST is a general index which could support multiple types of probabilistic queries. Theoretical analysis and extensive experimental results demonstrate the effectiveness and efficiency of the proposed index.
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