Treating Insomnia, Amnesia, and Acalculia in Regular Expression Matching

TL;DR

This paper introduces Parallel Finite State Machines (PFSMs) that enable fast, distributed regular expression matching without prior automata conversion, effectively addressing amnesia, acalculia, and insomnia issues.

Contribution

The paper presents PFSMs, a novel automaton model that performs direct, online regex matching in parallel, overcoming limitations of traditional automata conversion and compaction.

Findings

PFSMs operate in quadratic time and linear memory.

They enable online detection of all matches within input strings.

PFSMs facilitate fast distributed regex matching in data-intensive environments.

Abstract

Regular expressions provide a flexible means for matching strings and they are often used in data-intensive applications. They are formally equivalent to either deterministic finite automata (DFAs) or nondeterministic finite automata (NFAs). Both DFAs and NFAs are affected by two problems known as amnesia and acalculia, and DFAs are also affected by a problem known as insomnia. Existing techniques require an automata conversion and compaction step that prevents the use of existing automaton databases and hinders the maintenance of the resulting compact automata. In this paper, we propose Parallel Finite State Machines (PFSMs), which are able to run any DFA- or NFA-like state machines without a previous conversion or compaction step. PFSMs report, online, all the matches found within an input string and they solve the three aforementioned problems. Parallel Finite State Machines require quadratic time and linear memory and they are distributable. Parallel Finite State Machines make very fast distributed regular expression matching in data-intensive applications feasible.