在大数据时代,数据图的规模急剧增长,增量图模式匹配算法能够在数据图或模式图发生变化时避免重新在整个数据图上进行匹配、减少响应时间,因此成为了研究的热点.针对实际应用中数据图不变而模式图发生变化的情况,提出了一种面向模式图变化的增量图模式匹配算法PGC_IncGPM,在模式图匹配的过程中记录适当的中间结果作为索引,用于后续的模式匹配.提出了增强的图模式匹配算法GPMS,用于首次整个数据图上的模式匹配.该算法一方面能够建立后续增量匹配所需的索引,另一方面减少了整个数据图匹配的执行时间.设计实现了面向模式图增边和减边的两个核心子算法,通过子算法的组合,能够支持在模式图发生各种变化时进行增量图模式匹配.在真实数据集和合成数据集上进行实验,结果表明:与重新在整个数据图上进行匹配的ReComputing算法相比,当模式图中变化的边的数目不超过不变的边的数目时,PGC_IncGPM算法能够有效减少图模式匹配的执行时间;随着数据图规模的增大,PGC_IncGPM算法相对于ReComputing算法的执行时间的减少程度更加明显,对于大规模数据图具有更好的适用性.

In the age of big data, with the scales of data graphs growing rapidly, incremental graph pattern matching algorithm has become the research focus since it can avoid re-computing matches in the whole data graph and reduce the response time when the data graph or the pattern graph update. Considering the scenario where the data graph is constant while the pattern graph is changing in practical application, a pattern graph change oriented incremental graph pattern matching algorithm named PGC_IncGPM is proposed. The appropriate intermediate results generated in the process of graph pattern matching are recorded as indexes for subsequent pattern matching. An enhanced graph pattern matching algorithm named GPMS is presented for the first time for graph matching in the whole data graph. On one hand, the algorithm can build indexes for the subsequent incremental matching; on the other hand, it reduces the execution time of matching in the data graph. Two core subalgorithms designated to adding and reducing edges in the patter graph are designed and realized. No matter what mode changes in the pattern graph, incremental graph pattern matching can be carried out by combining these two subalgorithms. Experiments on real datasets and synthetic data show that PGC_IncGPM can effectively reduce the graph pattern matching execution time. Compared with the ReComputing algorithm which re-computes matches starting from scratch in the whole data graph, if the number of changed edges does not exceed the number of unchanged edges, the proposed algorithm can reduce execution time effectively. With the scale of the data graph increases, the reduction in execution time of PGC_IncGPM algorithm is more obvious, demonstrating the algorithm's applicability for large-scale data graph.

国家自然科学基金(61232001, 61173169); 湖南省教育厅项目(15C0824)