Learning state machines via efficient hashing of future traces

TL;DR

This paper introduces a memory-efficient method for learning state machines from streaming data by combining count-min-sketch data structures with state merging techniques, enabling scalable analysis of large datasets.

Contribution

It presents a novel approach that integrates count-min-sketch with state merging to efficiently learn state machines from data streams, reducing memory usage and maintaining result quality.

Findings

Effective in reducing memory requirements

Maintains high quality of learned state machines

Demonstrates improved run-time performance

Abstract

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.