Hi all,

I would like to share with you a part of my undergraduate thesis on a Multi-String Pattern Matcher data structure. In my opinion, it's easy to understand and hard to implement correctly and efficiently. It's competitive against other MSPM data structures (Aho-Corasick, suffix array/automaton/tree to name a few) when the dictionary size is specifically (uncommonly) large.

I would also like to sign up this entry to bashkort's Month of Blog Posts:-)

Abstract

This work describes a hash-based mass-searching algorithm, finding (count, location of first match) entries from a dictionary against a string $$$s$$$ of length $$$n$$$. The presented implementation makes use of all substrings of $$$s$$$ whose lengths are powers of $$$2$$$ to construct an offline algorithm that can, in some cases, reach a complexity of $$$O(n \log^2n)$$$ even if there are $$$O(n^2)$$$ possible matches. If there is a limit on the dictionary size $$$m$$$, then the precomputation complexity is $$$O(m + n \log^2n)$$$, and the search complexity is bounded by $$$O(n\log^2n + m\log n)$$$, even if it performs in practice like $$$O(n\log^2n + \sqrt{nm}\log n)$$$. Other applications, such as finding the number of distinct substrings of $$$s$$$ for each length between $$$1$$$ and $$$n$$$, can be done with the same algorithm in $$$O(n\log^2n)$$$.

Problem Description

We want to write an offline algorithm for the following problem, which receives as input a string $$$s$$$ of length $$$n$$$, and a dictionary $$$ts = \{t_1, t_2, .., t_{\lvert ts \rvert}\}$$$. As output, it expects for each string $$$t$$$ in the dictionary the number of times it is found in $$$s$$$. We could also ask for the position of the fist occurrence of each $$$t$$$ in $$$s$$$, but the paper mainly focuses on the number of matches.

Algorithm Description

We will build a DAG in which every node is mapped to a substring from $$$s$$$ whose length is a power of $$$2$$$. We will draw edges between any two nodes whose substrings are consecutive in $$$s$$$. The DAG has $$$O(n \log n)$$$ nodes and $$$O(n \log^2 n)$$$ edges.

We will break every $$$t_i \in ts$$$ down into a chain of substrings of $$$2$$$-exponential length in strictly decreasing order (e.g. if $$$\lvert t \rvert = 11$$$, we will break it into $$$\{t[1..8], t[9..10], t[11]\}$$$). If $$$t_i$$$ occurs $$$k$$$ times in $$$s$$$, we will find $$$t_i$$$'s chain $$$k$$$ times in the DAG.

Figure 1: The DAG for $$$s = (ab)^3$$$. If $$$ts = \{aba, baba, abb\}$$$, then $$$t_0 = aba$$$ is found twice in the DAG, $$$t_1 = baba$$$ once, and $$$t_2 = abb$$$ zero times.

Redundancy Elimination: Associated Trie, Tokens, Trie Search

A generic search for $$$t_0 = aba$$$ in the DAG would check if any node marked as $$$ab$$$ would have a child labeled as $$$a$$$. $$$t_2 = abb$$$ is never found, but a part of its chain is ($$$ab$$$). We have to check all $$$ab$$$s to see if any may continue with a $$$b$$$, but we have already checked if any $$$ab$$$s continue with an $$$a$$$ for $$$t_0$$$, making second set of checks redundant.

Figure 2: If the chains of $$$t_i$$$ and $$$t_j$$$ have a common prefix, it is inefficient to count the number of occurrences of the prefix twice. We will put all the $$$t_i$$$ chains in a trie. We will keep the hashes of the values on the trie edges.

In order to generalize all of chain searches in the DAG, we will add a starter node that points to all other nodes in the DAG. Now all DAG chains begin in the same node.

The actual search will go through the trie and find help in the DAG. The two Data Structures cooperate through tokens. A token is defined by both its value (the DAG index in which it’s at), and its position (the trie node in which it’s at).