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The big graph database model provides strong modeling for complexapplications and efficient querying. However, it is still a big challenge tofind all exact matches of a query graph in a big graph database, which is knownas the subgraph isomorphism problem. The current subgraph isomorphismapproaches are built on Ullmann's idea of focusing on the strategy of pruningout the irrelevant candidates. Nevertheless, the existing pruning techniquesneed much more improvement to efficiently handle complex queries. Moreover,many of those existing algorithms need large indices requiring extra memoryconsumption. Motivated by these, we introduce a new subgraph isomorphismalgorithm, named as BB-Graph, for querying big graph databases efficientlywithout requiring a large data structure to be stored in main memory. We testand compare our proposed BB-Graph algorithm with two popular existingapproaches, GraphQL and Cypher. Our experiments are done on three differentdata sets; (1) a very big graph database of a real-life population database,(2) a graph database of a simulated bank database, and (3) the publiclyavailable World Cup big graph database. We show that our solution performsbetter than those algorithms mentioned here for most of the query typesexperimented on these big databases.

BB-Graph: A Subgraph Isomorphism Algorithm for Efficiently Querying Big Graph Databases