Abstract

Due to rapid growth of the Internet technology and new scientific/technological advances, the number of applications that model data as graphs increases, because graphs have high expressive power to model complicated structures. Greedy algorithms for subgraph pattern matching operations are often sufficient when the graph data set can be held in memory on a single machine. However, as graph data sets increasingly expand and require external storage and partitioning across a cluster of machines, more sophisticated query optimization techniques become critical to avoid explosions in query latency. In this paper, there is query optimization technique for distributed graph pattern matching

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