在奥得奥

摘要：

布隆过滤器是一种数据结构，用于确定元素的集合成员关系，其应用范围从字符串匹配到网络和安全问题。这些结构的青睐，因为它们减少了内存消耗和快速挂钟和渐近的时间界限。一般来说，Bloom过滤器保持恒定的成员查询时间，使它们在其利基中非常快。然而，它们的局限性在于缺乏清除操作，以及它们的概率性质。在本文中，我们讨论了各种迭代和替代品的通用布隆过滤器，已被研究和实施，以克服其固有的局限性。布隆过滤器，特别是当与其他数据结构结合使用时，仍然是强大而有效的数据结构;我们进一步讨论它们在工业和研究中的使用，以优化资源利用率。

英文摘要：

Bloom filters are data structures used to determine set membership of elements, with applications from string matching to networking and security problems. These structures are favored because of their reduced memory consumption and fast wallclock and asymptotic time bounds. Generally, Bloom filters maintain constant membership query time, making them very fast in their niche. However, they are limited in their lack of a removal operation, as well as by their probabilistic nature. In this paper, we discuss various iterations of and alternatives to the generic Bloom filter that have been researched and implemented to overcome their inherent limitations. Bloom filters, especially when used in conjunction with other data structures, are still powerful and efficient data structures; we further discuss their use in industy and research to optimize resource utilization.

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。