Scalable storage structure for pattern matching on big graph data

The wide popularity of graphs in areas such as Semantic Web and Social Network has necessitated the need to develop efficient methods to store and process graph data. However, the unique structure of graphs render traditional data handling methods and storage structures inefficient when dealing with large volumes of data. Existing graph storage structures either compromise scalability by adopting an in-memory approach or compromise on performance by relying heavily on disk access. In this paper, we propose a novel graph data storage format that reduces latency due to disk access by using a hybrid storage strategy. We also introduce an adaptive caching technique that makes the structure scalable to accommodate large scale graphs.