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State machines are popular models to model and visualize discrete systemssuch as software systems, and to represent regular grammars. Most algorithmsthat passively learn state machines from data assume all the data to beavailable from the beginning and they load this data into memory. This makes ithard to apply them to continuously streaming data and results in large memoryrequirements when dealing with large datasets. In this paper we propose amethod to learn state machines from data streams using the count-min-sketchdata structure to reduce memory requirements. We apply state merging using thewell-known red-blue-framework to reduce the search space. We implemented ourapproach in an established framework for learning state machines, and evaluatedit on a well know dataset to provide experimental data, showing theeffectiveness of our approach with respect to quality of the results andrun-time.

Learning state machines via efficient hashing of future traces