AI 中文总结
针对多重集成员查询，现有方案有缺陷。本文提出STEM2，用平衡二叉树架构及新型分离器，采用最小化哈希方案，分离控制和数据平面。该结构查询准确、支持动态更新，实验显示其性能远超同类，兼具低内存成本和高正确性。
AI 中文摘要
多重集成员查询在网络和数据库系统中普遍存在。当前解决方案存在难题：哈希表保证正确性但内存占用大，基于过滤器的方法以概率性错误为代价优化空间。本文提出STEM2，一种快速且节省空间的数据结构，实现100%查询准确性并支持多重集成员查询的动态键更新。它采用平衡二叉树架构，非叶节点包含新型精确二进制集分离器将键分成两个不相交组。设计的关键创新是最小化哈希方案，每次键查找仅需两次哈希计算，显著减少计算开销。此外，STEM2分离控制平面和数据平面，控制平面处理构建和动态更新，数据平面致力于高效成员查询。大量实验表明，STEM2查找吞吐量达每秒超1.2亿次操作，比现有技术的Coloring Embedder快20%，比Ludo哈希快达21.6倍，同时保持紧凑内存成本和精确正确性。
英文摘要
Multi-set membership queries are ubiquitous in networking and database systems. Current solutions force a difficult compromise: hash tables guarantee correctness but suffer from high memory footprints, while filter-based approaches optimize space at the cost of probabilistic errors. In this paper, we propose STEM2, a fast and space-efficient data structure that achieves 100% query accuracy and can support dynamic key updates for multi-set membership queries. STEM2 utilizes a balanced binary tree architecture where each non-leaf node incorporates a novel Exact Binary Set Separator (XBSS) to partition keys into two disjoint groups. A key innovation of our design is a minimized hashing scheme that requires only two hash computations per key lookup, significantly reducing computational overhead. Additionally, STEM2 separates the control plane and the data plane: the control plane handles construction and dynamic updates, while the data plane is dedicated to serving efficient membership queries. Extensive experiments show that STEM2 achieves over 120 million operations per second (Mops) in lookup throughput, outperforming the state-of-the-art Coloring Embedder by 20% and the Ludo hashing by up to 21.6X, while maintaining compact memory cost and exact correctness.
Journal refPVLDB, 19(9): 2426-2438, 2026
