On the Practical Power of Automata in Pattern Matching

TL;DR

This paper compares the practical efficiency of the KMP automaton and naive pattern matching algorithms on random and DNA texts, revealing that KMP outperforms naive matching in parameterized matching scenarios, contrary to traditional expectations.

Contribution

It provides a comprehensive analysis of the KMP automaton's performance in parameterized pattern matching on random and biological texts, highlighting surprising efficiency gains.

Findings

KMP automaton is faster than naive in parameterized matching on random texts.

In exact matching, naive and KMP perform similarly, confirming folklore.

Structured cases can significantly improve automaton efficiency.

Abstract

The classical pattern matching paradigm is that of seeking occurrences of one string - the pattern, in another - the text, where both strings are drawn from an alphabet set $\Sigma$. Assuming the text length is $n$ and the pattern length is $m$, this problem can naively be solved in time $O(nm)$. In Knuth, Morris and Pratt's seminal paper of 1977, an automaton, was developed that allows solving this problem in time $O(n)$ for any alphabet. This automaton, which we will refer to as the {\em KMP-automaton}, has proven useful in solving many other problems. A notable example is the {\em parameterized pattern matching} model. In this model, a consistent renaming of symbols from $\Sigma$ is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been suspected that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper we examine the practical efficiency of the KMP algorithm vs. the naive algorithm on a randomly generated text. We analyse the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts, and observe a similar behaviour as in the random text. We also show a very structured case where the automaton is much more efficient.