Learning state machines via efficient hashing of future traces
This work addresses memory efficiency for learning state machines in streaming applications, but it is incremental as it builds on existing frameworks and methods.
The paper tackles the problem of learning state machines from streaming data with high memory requirements by proposing a method using count-min-sketch to reduce memory usage and applying state merging with the red-blue-framework to limit search space. The result shows effectiveness in quality and run-time on a known dataset, though no concrete numbers are provided.
State machines are popular models to model and visualize discrete systems such as software systems, and to represent regular grammars. Most algorithms that passively learn state machines from data assume all the data to be available from the beginning and they load this data into memory. This makes it hard to apply them to continuously streaming data and results in large memory requirements when dealing with large datasets. In this paper we propose a method to learn state machines from data streams using the count-min-sketch data structure to reduce memory requirements. We apply state merging using the well-known red-blue-framework to reduce the search space. We implemented our approach in an established framework for learning state machines, and evaluated it on a well know dataset to provide experimental data, showing the effectiveness of our approach with respect to quality of the results and run-time.