bloomRF：用分段单调散列函数和前缀散列实现布隆滤波器的范围扩展
bloomRF: On Performing Range-Queries in Bloom-Filters with Piecewise-Monotone Hash Functions and Prefix Hashing
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。作为第一个核心思想，bloomRF引入了新的前缀散列，以有效地将范围信息编码在密钥本身的散列代码中。作为第二个关键概念，bloomRF提出了新的分段单调散列函数，保持局部顺序，并支持快速范围查找，内存访问较少。bloomRF具有接近最优的空间复杂度和恒定的查询复杂度。虽然bloomRF是为整数域设计的，但它支持浮点数，并且可以用作多属性过滤器。在RocksDB和独立库中的评估表明，它更有效，在一系列设置和分布中比现有的点范围过滤器性能高出4倍，同时保持低误报率。
英文摘要：
We introduce bloomRF as a unified method for approximate membership testing that supports both point- and range-queries. As a first core idea, bloomRF introduces novel prefix hashing to efficiently encode range information in the hash-code of the key itself. As a second key concept, bloomRF proposes novel piecewise-monotone hash-functions that preserve local order and support fast range-lookups with fewer memory accesses. bloomRF has near-optimal space complexity and constant query complexity. Although, bloomRF is designed for integer domains, it supports floating-points, and can serve as a multi-attribute filter. The evaluation in RocksDB and in a standalone library shows that it is more efficient and outperforms existing point-range-filters by up to 4x across a range of settings and distributions, while keeping the false-positive rate low.
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。
