Efficient Dynamic Dictionary Matching with DAWGs and AC-automata

TL;DR

This paper introduces two efficient algorithms for dynamic dictionary matching using DAWGs and AC-automata, supporting pattern insertions and deletions with optimized update and matching times.

Contribution

It presents novel algorithms that improve dynamic dictionary matching efficiency by leveraging DAWGs and AC-automata, supporting both insertions and deletions.

Findings

Supports pattern insertions in O(m log σ + log d / log log d) time

Supports pattern deletions with optimized time complexity

Achieves optimal update time for AC-automaton based methods over constant alphabets

Abstract

The dictionary matching is a task to find all occurrences of patterns in a set $D$ (called a dictionary) on a text $T$. The Aho-Corasick-automaton (AC-automaton) is a data structure which enables us to solve the dictionary matching problem in $O(d\log\sigma)$ preprocessing time and $O(n\log\sigma+occ)$ matching time, where $d$ is the total length of the patterns in $D$, $n$ is the length of the text, $\sigma$ is the alphabet size, and $occ$ is the total number of occurrences of all the patterns in the text. The dynamic dictionary matching is a variant where patterns may dynamically be inserted into and deleted from $D$. This problem is called semi-dynamic dictionary matching if only insertions are allowed. In this paper, we propose two efficient algorithms. For a pattern of length $m$, our first algorithm supports insertions in $O(m\log\sigma+\log d/\log\log d)$ time and pattern matching in $O(n\log\sigma+occ)$ time for the semi-dynamic setting and supports both insertions and deletions in $O(\sigma m+\log d/\log\log d)$ time and pattern matching in $O(n(\log d/\log\log d+\log\sigma)+occ(\log d/\log\log d))$ time for the dynamic setting by some modifications. This algorithm is based on the directed acyclic word graph. Our second algorithm, which is based on the AC-automaton, supports insertions in $O(m\log \sigma+u_f+u_o)$ time for the semi-dynamic setting and supports both insertions and deletions in $O(\sigma m+u_f+u_o)$ time for the dynamic setting, where $u_f$ and $u_o$ respectively denote the numbers of states in which the failure function and the output function need to be updated. This algorithm performs pattern matching in $O(n\log\sigma+occ)$ time for both settings. Our algorithm achieves optimal update time for AC-automaton based methods over constant-size alphabets, since any algorithm which explicitly maintains the AC-automaton requires $\Omega(m+u_f+u_o)$ update time.