SQM:基于Spark的大规模单图上的子图匹配算法

针对大规模数据图下基于回溯法的子图查询算法的准确率低、开销大等问题,为提高查询准确率,降低大图下的查询开销,提出一种基于Spark的子图匹配(SQM)算法。首先根据结构信息过滤数据图,再将查询图分割成基本查询单元;然后对每一个基本查询单元分别匹配后进行Join操作;最后运用并行化提高了算法的运行效率,减小了搜索空间。实验结果表明,与Stwig、Turbo ISO算法相比,SQM算法在保证查询结果不变的情况下,速度提高了50%。

SQM: subgraph matching algorithm for single large-scale graphs under Spark

Focusing on low accuracy and high costs of backtracking-based subgraph query algorithm applied to large-scale graphs, a Spark-based Subgraph Query Matching (SQM) algorithm was proposed to improve query accuracy and reduce query overhead for large graphs. The data graph was firstly filtered according to structure information, and the query graph was divided into basic query units. Then each basic query unit was matched and joined together. Finally, the algorithm's efficiency was improved and search space was reduced by parallelization. The experimental results show that compared with Stwig (Sub twig) algorithm and TurboISO algorithm, SQM algorithm can increase the speed by 50% while ensuring the same query results.