一句话：通过估计查询结果上界进行剪枝，使演化图上的成对分析查询达到亚秒级延迟。
实时 OLAP 系统常追求亚秒级查询演化数据，但分析型图查询通常需要访问每个连通顶点，难以低延迟完成。论文指出，许多实际场景只需回答成对查询，因此可避免对全图的穷举，仅访问图的一小部分。实现低延迟的关键在于尽可能消除不必要计算，而现有系统通常通过估计查询结果的上界来完成这一剪枝过程。文章围绕该问题展开研究，分析成对查询的剪枝机制与上界估计对延迟的影响，目标是在演化图上实现亚秒级成对查询。摘要未给出具体系统名称与实验数据。
Many real-time OLAP systems have been proposed to query evolving data with sub-second latency. Although this feature is highly attractive, it is very hard to be achieved on analytic graph queries that can only be answered after accessing every connected vertex. Fortunately, researchers recently observed that answering pairwise queries is enough for many real-world scenarios. These pairwise queries avoid the exhaustive nature and hence may only need to access a small portion of the graph. Obviously, the crux of achieving low latency is to what extent the system can eliminate unnecessary computations. This pruning process, according to our investigation, is usually achieved by estimating certain upper bounds of the query result in existing systems.
@inproceedings{paperbot10040,
title = {Achieving Sub-second Pairwise Query over Evolving Graphs},
author = {Hongtao Chen and Mingxing Zhang and Ke Yang and Kang Chen and Albert Zomaya and Yongwei Wu and Xuehai Qian},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3575693.3576173}
}