Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach

TL;DR

This paper introduces a new optimized pattern matching algorithm based on Ukkonen's approach, significantly improving efficiency for large-scale text search tasks in natural language processing and bioinformatics.

Contribution

It presents a novel combination of Ukkonen's Algorithm with a new search technique, achieving linear time and space efficiency for suffix tree-based pattern matching.

Findings

Outperforms traditional methods like Naive Search, KMP, and Boyer-Moore in efficiency.

Achieves 100% accuracy in genomic pattern recognition.

Demonstrates practical utility in natural language processing and bioinformatics.

Abstract

In the realm of computer science, the efficiency of text-search algorithms is crucial for processing vast amounts of data in areas such as natural language processing and bioinformatics. Traditional methods like Naive Search, KMP, and Boyer-Moore, while foundational, often fall short in handling the complexities and scale of modern datasets, such as the Reuters corpus and human genomic sequences. This study rigorously investigates text-search algorithms, focusing on optimizing Suffix Trees through methods like Splitting and Ukkonen's Algorithm, analyzed on datasets including the Reuters corpus and human genomes. A novel optimization combining Ukkonen's Algorithm with a new search technique is introduced, showing linear time and space efficiencies, outperforming traditional methods like Naive Search, KMP, and Boyer-Moore. Empirical tests confirm the theoretical advantages, highlighting the optimized Suffix Tree's effectiveness in tasks like pattern recognition in genomic sequences, achieving 100% accuracy. This research not only advances academic knowledge in text-search algorithms but also demonstrates significant practical utility in fields like natural language processing and bioinformatics, due to its superior resource efficiency and reliability.