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BMF: An Indexing Structure to Support Multi-element Check

机译:BMF:支持多元素检查的索引结构

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Standard Bloom Filter is an efficient structure to check element membership with low space cost and low false positive rate. However, the standard Bloom Filter assumes that all elements belong to a single set. When given multiple sets of elements, it cannot efficiently check whether or not multiple input elements belong to the same set, called multi-element check. To support the multi-element check, in this paper, we design a new data structure, namely Bloom Multi-filter (BMF). BMF maintains an array of integer numbers to support (1) the insertion of multiple sets of elements into BMF and (2) the lookup to answer multi-element check. We propose four techniques to improve the BMF and optimize the false positive rate. We conducted intensive experiments to study the tradeoff between BMF's space cost and lookup precision. Our experimental results indicate that BMF greatly outperforms the standard bloom filters with around 9.82 folds of lower false positive rate.
机译:标准布隆过滤器是一种有效的结构,它以较低的空间成本和较低的误报率来检查元素成员资格。但是,标准的布隆过滤器假定所有元素都属于一个集合。当给定多组元素时,它无法有效地检查多个输入元素是否属于同一组,这称为多元素检查。为了支持多元素检查,在本文中,我们设计了一种新的数据结构,即Bloom Multi-filter(BMF)。 BMF维护一个整数数组,以支持(1)将多组元素插入BMF,以及(2)查找以回答多元素检查。我们提出了四种技术来改善BMF和优化误报率。我们进行了深入的实验,以研究BMF的空间成本与查找精度之间的权衡。我们的实验结果表明,BMF大大优于标准Bloom过滤器,其误报率更低,约为9.82倍。

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