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首页> 外文期刊>IEEE Transactions on Knowledge and Data Engineering >Set Reconciliation via Counting Bloom Filters
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Set Reconciliation via Counting Bloom Filters

机译:通过计算布隆过滤器设置对帐

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In this paper, we study the set reconciliation problem, in which each member of a node pair has a set of objects and seeks to deliver its unique objects to the other member. How could each node compute the set difference, however, is challenging in the set reconciliation problem. To address such an issue, we propose a lightweight but efficient method that only requires the pair of nodes to represent objects using a counting Bloom filter (CBF) of size $(O(d))$ and exchange with each other, where $(d)$ denotes the total size of the set differences. A receiving node then subtracts the received CBF from its local one via minus operation proposed in this paper. The resultant CBF can approximately represent the union of the set differences and thus the set difference to each node can be identified after querying the resultant CBF. In this paper, we propose a novel estimator through which each node can accurately estimate not only the value of $(d)$ but also the size of the set difference to each node. Such an estimation result can be used to optimize the parameter setting of the CBF to achieve less false positives and false negatives. Comprehensive analysis and evaluation demonstrates that our method is more efficient than prior BF-based methods in terms of achieving the same accuracy with less communication cost. Moreover, our reconciliating method needs no prior context logs and it is very useful in networking and distributed applications.
机译:在本文中,我们研究了集合和解问题,其中一个节点对的每个成员都有一组对象,并试图将其唯一的对象传递给另一个成员。然而,每个节点如何计算集合差异在集合对帐问题中具有挑战性。为了解决这个问题,我们提出了一种轻量级但有效的方法,该方法仅要求一对节点使用大小为$ {O(d))$的计数布隆过滤器(CBF)表示对象并相互交换,其中$ { d)$表示设定差异的总大小。然后,接收节点通过本文提出的减法运算从本地CBF中减去接收到的CBF。所得的CBF可以近似表示集合差异的并集,因此可以在查询所得的CBF之后识别到每个节点的集合差异。在本文中,我们提出了一种新颖的估计器,通过该估计器,每个节点不仅可以准确估计$(d)$的值,而且可以精确估计每个节点的集差的大小。这样的估计结果可以用于优化CBF的参数设置,以实现更少的假阳性和假阴性。全面的分析和评估表明,我们的方法比以前的基于BF的方法更有效,从而能够以更低的通信成本实现相同的准确性。而且,我们的协调方法不需要事先的上下文日志,在网络和分布式应用程序中非常有用。

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