bloomRF：基于分段单调散列函数和二元迹树的布隆滤波器距离估计
bloomRF: On Performing Range-Queries with Bloom-Filters based on Piecewise-Monotone Hash Functions and Dyadic Trace-Trees
摘要：
我们引入bloomRF作为一个统一的方法，近似的成员资格测试，支持点和范围查询一个单一的数据结构。bloomRF扩展了Bloom-Filters的范围查询支持，并可能取代它们。其核心思想是采用二元区间方案来确定覆盖数据点的二元区间集，然后对其进行编码和插入。bloomRF引入了二元跟踪树作为新的数据结构，它隐式地表示那些覆盖区间。跟踪树编码方案以紧凑的位表示有效地表示覆盖区间的集合。此外，bloomRF引入了新颖的分段单调散列函数，这些散列函数是局部保序的，因此支持范围查询。我们提出了一个有效的范围查询的成员计算方法。尽管bloomRF是为整数设计的，但它也支持字符串和浮点数据类型。它还可以处理多个属性，并作为多属性过滤器。我们在RocksDB和一个独立的库中评估bloomRF。bloomRF更高效，在一系列设置中的性能最高可达现有点范围过滤器的4倍。
英文摘要：
We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries on a single data structure. bloomRF extends Bloom-Filters with range query support and may replace them. The core idea is to employ a dyadic interval scheme to determine the set of dyadic intervals covering a data point, which are then encoded and inserted. bloomRF introduces Dyadic Trace-Trees as novel data structure that represents those covering intervals implicitly. A Trace-Tree encoding scheme represents the set of covering intervals efficiently, in a compact bit representation. Furthermore, bloomRF introduces novel piecewise-monotone hash functions that are locally order-preserving and thus support range querying. We present an efficient membership computation method for range-queries. Although, bloomRF is designed for integers it also supports string and floating-point data types. It can also handle multiple attributes and serve as multi-attribute filter. We evaluate bloomRF in RocksDB and in a standalone library. bloomRF is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
