Nowadays, the scale of various graphs soars rapidly, which imposes a serious challenge to develop processing and analytic algorithms. Among them, graph pattern matching is the one of the most primitive tasks that find a wide spectrum of applications, the performance of which is yet often affected by the size and dynamicity of graphs. In order to handle large dynamic graphs, incremental pattern matching is proposed to avoid re-computing matches of patterns over the entire data graph, hence reducing the matching time and improving the overall execution performance. Due to the complexity of the problem, little work has been reported so far to solve the problem, and most of them only solve the graph pattern matching problem under the scenario of the data graph varying alone. In this article, we are devoted to a more complicated but very practical graph pattern matching problem, continuous matching of evolving patterns over dynamic graph data, and the investigation presents a novel algorithm CEPDG for continuously pattern matching along with changes of both pattern graph and data graph. Specifically, we propose a concise representation TreeMat of partial matching solutions, which can help to avoid re-computing matches of the pattern and speed up subsequent matching process. In order to enable the updates of data graph and pattern graph, we propose an incremental maintenance strategy, to efficiently maintain the intermediate results. Moreover, we conceive an effective model for estimating step-wise cost of pattern evaluation to drive the matching process. Extensive experiments verify the superiority of CEPDG.

如今，各种图形的比例迅速飙升，这对开发处理和分析算法提出了严峻的挑战。其中，图模式匹配是发现各种应用程序的最原始的任务之一，其性能通常受图的大小和动态性的影响。为了处理大型动态图，提出了增量模式匹配，以避免在整个数据图上重新计算模式匹配，从而减少了匹配时间并提高了整体执行性能。由于问题的复杂性，迄今为止解决该问题的工作很少，而且大多数解决方案仅在数据图单独变化的情况下解决图模式匹配问题。在本文中，动态图数据上不断变化的模式的连续匹配，并且研究提出了一种新的CEPDG算法，用于连续模式匹配以及模式图和数据图的变化。具体来说，我们提出了一个简洁的表示TreeMat的部分匹配解决方案，它可以帮助避免重新计算模式的匹配并加快后续的匹配过程。为了能够更新数据图和模式图，我们提出了一种增量维护策略，以有效地维护中间结果。此外，我们构想出一种有效的模型，用于估算模式评估的逐步成本，以驱动匹配过程。大量实验证明了CEPDG的优越性。