The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. Traditional approaches to this problem were developed around the notion of Incremental View Maintenance (IVM), and are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques are suboptimal: instead of materializing results and subresults, one may also maintain a data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In two previous articles, we have presented algorithms for dynamically evaluating queries that are easy to implement, efficient, and can be naturally extended to evaluate queries from a wide range of application domains. In this paper, we discuss our algorithm and its complexity, explaining the main components behind its efficiency. Finally, we show experiments that compare our algorithm to a state-of-the-art (Higher-order) IVM engine, as well as to a prominent complex event recognition engine. Our approach outperforms the competitor systems by up to two orders of magnitude in processing time, and one order in memory consumption.

中文翻译：

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动态变化数据集的高效查询处理

高效分析不断变化的数据的能力是许多实时分析应用程序的关键要求。解决这个问题的传统方法是围绕增量视图维护 (IVM) 的概念开发的，并且基于子结果的物化（以避免重新计算）或子结果的重新计算（以避免物化的空间开销）。这两种技术都不是最理想的：除了具体化结果和子结果之外，还可以维护一种数据结构，该数据结构支持更新时的有效维护，并且可以从中快速枚举完整的查询结果。在之前的两篇文章中，我们介绍了用于动态评估查询的算法，这些算法易于实现、高效，并且可以自然地扩展以评估来自广泛应用领域的查询。在本文中，我们讨论了我们的算法及其复杂性，解释了其效率背后的主要组成部分。最后，我们展示了将我们的算法与最先进的（高阶）IVM 引擎以及著名的复杂事件识别引擎进行比较的实验。我们的方法在处理时间和内存消耗方面比竞争对手的系统高出两个数量级。