| dc.description.abstract | The problem of multiple string matching is fundamental in computer science, with applications in bioinformatics, text mining, and information retrieval. Traditional methods struggle with large datasets due to high computational and memory requirements. This research proposes a novel algorithm that combines the Burrows-Wheeler Transform (BWT) for text compression and the Wavelet Matrix (WM) for efficient pattern search. The proposed method achieves faster search times, lower memory usage, and effective compression, particularly for repetitive datasets like DNA sequences. Experimental results demonstrate that the method performs better compared to existing algorithms. This work contributes to the advancement of efficient and scalable multiple string-matching techniques, with potential applications in large-scale text processing and bioinformatics.
Keywords: Algorithms, Text Compression, Wavelet Matrix, Burrows-Wheeler transform, Multiple String matching | en_US |
