Fast Circular Pattern Matching

TL;DR

This paper introduces a novel approach to the Exact Circular Pattern Matching problem, focusing on efficiently finding all rotations of a pattern in a text, with practical data structures and experimental evaluation.

Contribution

It presents four new data structures for ECPM, offering different time-space trade-offs and includes experimental analysis to identify the most practical solution.

Findings

Four data structures with various trade-offs

Experimental results on computational feasibility

Enhanced efficiency in circular pattern matching

Abstract

The Exact Circular Pattern Matching (ECPM) problem consists of reporting every occurrence of a rotation of a pattern $P$ in a text $T$. In many real-world applications, specifically in computational biology, circular rotations are of interest because of their prominence in virus DNA. Thus, given no restrictions on pre-processing time, how quickly all such circular rotation occurrences is of interest to many areas of study. We highlight, to the best of our knowledge, a novel approach to the ECPM problem and present four data structures that accompany this approach, each with their own time-space trade-offs, in addition to experimental results to determine the most computationally feasible data structure.