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The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining anddoi:10.13016/m2pbbk-rlsp fatcat:ilrtm23jkfgj5mgwtg2fnvippy