多集合的布隆过滤器变体：比较评估
Bloom filter variants for multiple sets: a comparative assessment
摘要：
在本文中，我们比较了两个概率数据结构的关联查询派生出著名的布隆过滤器：移位布隆过滤器（ShBF），和空间布隆过滤器（SBF）。相对于原始数据结构，两种变体都增加了使用不同策略在同一过滤器中存储多个子集的能力。我们分析了这两种数据结构在误报概率和集合间错误概率（集合中的元素被识别为属于错误子集的概率）方面的性能。作为我们分析的一部分，我们扩展了移位Bloom过滤器的功能，优化了任何非平凡数量的子集的过滤器。我们提出了一个新的广义ShBF定义与我们的特定域之外的应用，并提出了新的概率公式。比较的结果表明，ShBF提供了更好的空间效率，但在一个显着更高的计算成本比SBF。
英文摘要：
In this paper we compare two probabilistic data structures for association queries derived from the well-known Bloom filter: the shifting Bloom filter (ShBF), and the spatial Bloom filter (SBF). With respect to the original data structure, both variants add the ability to store multiple subsets in the same filter, using different strategies. We analyse the performance of the two data structures with respect to false positive probability, and the inter-set error probability (the probability for an element in the set of being recognised as belonging to the wrong subset). As part of our analysis, we extended the functionality of the shifting Bloom filter, optimising the filter for any non-trivial number of subsets. We propose a new generalised ShBF definition with applications outside of our specific domain, and present new probability formulas. Results of the comparison show that the ShBF provides better space efficiency, but at a significantly higher computational cost than the SBF.
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