Set representation is a crucial functionality in various areas such as networking and databases. In many applications, memory and time constraints allow only an approximate representation where errors can appear for some queried elements. The Variable-Increment Counting Bloom Filter (VI-CBF) is a popular data structure for the representation of dynamically-changing sets, achieving a good tradeoff between memory efficiency and queries accuracy. For some applications, the required accuracy is higher than that enabled by the VI-CBF. In this paper, we present the Tandem Counting Bloom Filter (T-CBF), a new data structure that relies on the interaction among counters to describe sets with higher accuracy. We analyze its performance and show that by a joint consideration of counters, the T-CBF always performs better than the VI-CBF and it can for some configurations reduce its false positive probability by an order of magnitude. The overhead of such an approach is expressed upon an element insertion or query as read or write operations to a pair of counters rather than a single counter in each hash location. The operations themselves also require considering a larger number of scenarios.

中文翻译：

---

串联计数布隆过滤器-探戈需要两个计数器

集合表示是网络和数据库等各个领域的重要功能。在许多应用程序中，内存和时间限制仅允许近似表示，其中某些查询元素可能会出现错误。可变增量计数布隆过滤器（VI-CBF）是一种流行的数据结构，用于表示动态变化的集，从而在内存效率和查询精度之间取得了良好的折衷。对于某些应用，所需的精度要高于VI-CBF所要求的精度。在本文中，我们提出了串联计数布隆过滤器（T-CBF），这是一种新的数据结构，该结构依赖于计数器之间的相互作用来描述具有更高准确性的集合。我们对其效果进行了分析，结果表明，通过对柜台的共同考虑，T-CBF的性能始终优于VI-CBF，并且对于某些配置，它可以将其误报率降低一个数量级。这种方法的开销在元素插入或查询时表示为对一对计数器而不是每个哈希位置中的单个计数器的读或写操作。这些操作本身还需要考虑大量方案。