Efficient search in graph databases using cross filtering

Recently, graph data has been increasingly used in many areas such as bio-informatics and social networks, and a large amount of graph data is generated in those areas. As such, we need to manage such data efficiently. A basic, common problem in graph-related applications is to find graph data that contains a query (Graph Query Problem). However, since examining graph data sequentially incurs a prohibitive cost due to subgraph isomorphism testing, a novel indexing scheme is needed. A feature-based approach is generally used as a graph indexing scheme. A path structure, a tree structure, or a graph structure can be extracted from a graph database as a feature. The path feature and the tree feature can be easily managed, but have lower pruning power than the graph feature. Although the graph feature has the best pruning power, it takes too much time to match the graph feature with the query. In this paper, we propose a graph feature-based approach called a CF-Framework (Cross Filtering-Framework) to solve the graph query problem efficiently. To select the graph features that correspond to the query with a low cost, the CF-Framework makes two feature groups according to the query and filters out each group crossly (i.e., alternately) based on set properties. We then validate the efficiency of the CF-Framework through experimental results.