Continuous multi-query optimization for subgraph matching over dynamic graphs

There is a growing need to perform real-time analytics on dynamic graphs in order deliver the values of big data users. An important problem from such applications continuously identifying and monitoring critical patterns when fine-grained updates at high velocity occur graphs. A lot efforts have been made develop practical solutions for these problems. Despite efforts, existing algorithms showed limited running time scalability dealing with large and/or many In this paper, we study continuous multi-query optimization subgraph matching over graph data. (1) We propose annotated query graph, which obtained by merging multi-queries into one. (2) Based query, employ concise auxiliary structure represent partial compact form. (3) addition, an efficient maintenance strategy detect affected queries each update report corresponding matches one pass. (4) Extensive experiments real-life synthetic datasets verify effectiveness efficiency our approach confirm two orders magnitude improvement proposed solution.