Efficient continuous subgraph matching scheme considering data reuse

Abstract

As the utilization of graph streams in various applications increases, a continuous subgraph matching scheme is required to search for subgraphs that change in real time. In this paper, we present a novel and effective continuous subgraph matching scheme that leverages indexing and employs distributed processing in graph stream environments. To achieve distributed processing, we employ a query graph decomposition policy based on the degree of nodes, allowing us to manage the decomposed subqueries as an index. By reusing indexing information, we significantly reduce the indexing load, which becomes crucial in scenarios where multiple queries are issued simultaneously. To optimize query allocation in the distributed environment, we introduce a cost model that accurately calculates the indexing load for each server. This ensures a balanced distribution of queries, enhancing overall system performance. To perform distributed processing efficiently in stream environments, the proposed scheme is implemented in Storm. We conduct various performance evaluations to demonstrate the superiority of the proposed scheme.