过滤器数据结构研究综述
Filter Data Structures:A Survey
王瀚橙 1戴海鹏 1陈树森 1陈志鹏 1陈贵海1
作者信息
- 1. 计算机软件新技术国家重点实验室(南京大学) 南京 210023
- 折叠
摘要
过滤器数据结构可以近似地判断某个元素是否属于给定集合.典型的过滤器数据结构,如布隆过滤器、布谷鸟过滤器、商过滤器,以牺牲查询准确性为代价换取更低的内存空间消耗和查询时间开销.因此,得益于空间时间高效性,过滤器数据结构现已被广泛应用于计算机网络、物联网、数据库系统、文件系统、生物信息学、机器学习等领域的近似成员资格查询操作中.自20世纪70年代以来,过滤器数据结构受到了广泛的研究,在诸多领域取得了重要的进展,其研究思路也在不断变化.文中整理了近五十年来关于过滤器数据结构的经典研究成果,从过滤器数据结构的原理出发对已有工作进行分类总结,并比较不同工作之间的引证关系和改进思路,最后讨论了过滤器数据结构的未来研究方向.
Abstract
Filter data structures can approximately determine whether an element exists in a given set.Typical filter data struc-tures,such as Bloom filters,cuckoo filters,and quotient filters,sacrifice query accuracy for lower memory space consumption and lower query time overhead.Due to their spatial and temporal efficiency,filter data structures are now widely used in approximate membership query operations in computer networks,the Internet of Things,database systems,file systems,bioinformatics,ma-chine learning,and other fields.Since the 1970s,filters have been extensively studied.Their research ideas are constantly chan-ging.This paper compiles the classic studies on filter data structures in the past fifty years,summarizes existing studies based on the mechanism of filter data structures and analyze the relationship between different studies.Finally,future research directions in filter data structures are discussed.
关键词
过滤器/近似成员资格查询/概率数据结构/布隆过滤器/布谷鸟过滤器/商过滤器Key words
Filter/Approximate membership query/Probabilistic data structure/Bloom filter/Cuckoo filter/Quotient filter引用本文复制引用
出版年
2024
NETL
NSTL
万方数据