We develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

我们开发了一系列有效的平面扫描间隔联接算法系列，用于评估各种间隔谓词，例如艾伦关系和参数化关系。我们的技术基于一个框架，该框架的组件可以以不同的方式灵活组合以支持所需的间隔关系。在时态数据库中，我们的算法可以利用众所周知的灵活访问方法（时间轴索引），从而进一步扩展其支持的操作集。另外，采用紧凑的数据结构，无间隙哈希图，我们有效地利用了CPU缓存。在实验评估中，我们证明了我们的方法比最先进的技术快几倍，并且扩展性更好，同时更适合于实时事件处理。