We study the hop-constrained s-t path enumeration (HcPE) problem, which takes a graph $G$, two distinct vertices $s,t$ and a hop constraint $k$ as input, and outputs all paths from $s$ to $t$ whose length is at most $k$. The state-of-the-art algorithms suffer from severe performance issues caused by the costly pruning operations during enumeration for the workloads with the large search space. Consequently, these algorithms hardly meet the real-time constraints of many online applications. In this paper, we propose PathEnum, an efficient index-based algorithm towards real-time HcPE. For an input query, PathEnum first builds a light-weight index aiming to reduce the number of edges involved in the enumeration, and develops efficient index-based approaches for enumeration, one based on depth-first search and the other based on joins. We further develop a query optimizer based on a join-based cost model to optimize the search order. We conduct experiments with 15 real-world graphs. Our experiment results show that PathEnum outperforms the state-of-the-art approaches by orders of magnitude in terms of the query time, throughput and response time.
翻译:我们研究的是高压路径查点(HcPE)问题,它需要一张G$图,两个不同的顶点,两个不同的顶点,两个不同的顶点,一个不同的顶点,一个不同的顶点,作为投入,一个不同的顶点,一个不同的顶点,一个的基点,一个的输入查询,PathEnum首先建立一个轻量指数,旨在减少查点所涉边点的数量,并发展基于指数的高效查点方法,一个基于深度搜索,另一个基于连接。我们进一步开发一个基于联合成本模型的查询优化器,以优化搜索秩序。我们用15个真实世界的图表进行实验。我们的实验结果显示,“路径Enum”超越了时间值的定点,通过时间值来测量。