论文标题
基于缓存效率的基于扫描的间隔与扩展的Allen关系谓词（扩展版本）
Cache-Efficient Sweeping-Based Interval Joins for Extended Allen Relation Predicates (Extended Version)
论文作者
论文摘要
我们开发了一个有效的扫地间隔算法的家族，可以评估诸如艾伦的关系和参数化关系之类的广泛间隔谓词。我们的技术基于一个框架，其组件可以以不同的方式灵活地组合，以支持所需的间隔关系。在时间数据库中，我们的算法可以利用一种众所周知且灵活的访问方法，即时间轴索引，从而扩展了其进一步支持的操作集。另外，采用紧凑的数据结构，无间隙哈希地图，我们有效地利用了CPU缓存。在实验评估中，我们表明我们的方法比最先进的技术要快几倍，并且比较缩放得更好，同时更适合实时事件处理。
We develop a family of efficient plane-sweeping interval join algorithms that can evaluate 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.
