时长约束的区间连接
Duration-constrained Interval Joins
AI总结：
针对现有区间连接算法未考虑重叠时长的问题，提出高效时长约束区间连接算法及两种优化技术，经实验验证其性能优于现有相关技术。
AI中文摘要：
包括时态、不确定、空间和轨迹数据库在内的许多数据库都使用区间数据，区间连接是最常用的算子之一。已有研究提出了高效的区间连接算法，但未考虑重叠时长，会返回任何区间对，即使它们的重叠程度极小，比如无实质关联或关系的情况。后续应用可能会受此类区间对影响，它们可能是噪声或分析不需要的内容，且输出这些对还会增加连接时间。为解决上述问题，本文研究时长约束的区间连接问题：给定两个区间集合R和S，以及重叠时长约束ε，返回所有满足r∈R、s∈S且r与s的重叠时长至少为ε的区间对(r,s)。直接方法是运行现有最优区间连接算法后再过滤符合条件的区间对，但该方法效率低下，会生成不必要的区间对并产生时长计算开销，无法解决效率问题。本文提出一种高效算法以消除上述缺陷，还提出两种优化技术提升算法效率。在三个真实区间数据集上开展的大量实验表明，本文算法优于所有适用于该问题的现有技术。
英文摘要：
Many databases, including temporal, uncertain, spatial, and trajectory databases, use interval data, and interval joins are among the most frequently used operators. Many studies proposed efficient interval join algorithms, but they do not consider the overlap duration. They return any pairs of intervals, even if they overlap very slightly, e.g., with no essential correlation or relationship. Subsequent applications may suffer from such interval pairs, as they may be noise or unnecessary for the analysis. Furthermore, outputting such pairs also increases join time. To address the above issues, this paper addresses the problem of duration-constrained interval join. Given two interval collections $R$ and $S$ and an overlap duration constraint $ε$, this problem returns all interval pairs $(r,s)$ such that $r \in R$, $s \in S$, and the overlap duration between $r$ and $s$ is at least $ε$. A straightforward approach for this problem is to run a state-of-the-art interval join algorithm and then filter qualified interval pairs. However, this is inefficient, as it generates unnecessary interval pairs and incurs duration computations, which cannot overcome the above efficiency concern. We propose an efficient algorithm for this problem that removes the above drawback. Furthermore, we propose two optimization techniques to improve the efficiency of our algorithm. We conduct extensive experiments on three real-world interval datasets, and the results demonstrate that our algorithm outperforms existing techniques applicable to our problem.
