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The growing popularity of dynamic applications such as social networks provides a promising way to detect valuable information in real time. Efficient analysis over high-speed data from dynamic applications is of great significance. Data from these dynamic applications can be easily modeled as streaming graph. In this paper, we study the subgraph (isomorphism) search over streaming graph data that obeys timing order constraints over the occurrence of edges in the stream. We propose a data structure and algorithm to efficiently answer subgraph search and introduce optimizations to greatly reduce the space cost, and propose concurrency management to improve system throughput. Extensive experiments on real network traffic data and synthetic social streaming data confirms the efficiency and effectiveness of our solution.
Subgraph matching is a compute-intensive problem that asks to enumerate all the isomorphic embeddings of a query graph within a data graph. This problem is generally solved with backtracking, which recursively evolves every possible partial embedding
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Many studies have been conducted on seeking the efficient solution for subgraph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Descript
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