A Benchmark Evaluation of Incremental Pattern Matching in Graph Transformation

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In graph transformation, the most cost-intensive phase of a transformation execution is pattern matching, where those subgraphs of a model graph are identified and matched which satisfy constraints prescribed by graph patterns. Incremental pattern matching aims to improve the efficiency of this critical step by storing the set of matches of a graph transformation rule and incrementally maintaining it as the model changes, thus eliminating the need of recalculating existing matches of a pattern. In this paper, we propose benchmark examples where incremental pattern matching is expected to have advantageous effect in the application domain of model simulation and model synchronization. Moreover, we compare the incremental graph pattern matching approach of Viatra2 with advanced non-incremental local-search based graph pattern matching approaches (as available in Viatra2 and GrGen).

This work was partially supported by the SENSORIA European project (IST-3-016004). The fourth author was also supported by the János Bolyai Scholarship.