An Enhancement of Boyer-Moore Algorithm Using Hash Table

Purpose This study aims to enhance the Boyer-Moore algorithm by integrating hash tables to improve efficiency in handling large datasets and complex patterns. The enhancement addresses challenges of increased execution time and memory usage in text processing tasks. Method The proposed algorithm incorporates hash tables to optimize pattern preprocessing and matching, enabling faster data access and reducing redundant computations. Performance was evaluated by testing both the original and enhanced algorithms on datasets ranging from 100,000 to over 5 million characters. Results The enhanced algorithm demonstrated a 16.39% improvement in execution time, processing 5,289,118 characters in 1.64 seconds compared to 1.96 seconds for the original. Memory usage was also reduced by approximately 1,210 KB across dataset sizes, showcasing its scalability and resource efficiency. Conclusion The integration of hash-based structures into the Boyer-Moore algorithm significantly enhances its runtime and memory performance without compromising accuracy. These improvements make it highly suitable for real-time applications such as content moderation, search engines, and large-scale data analytics. Recommendations Future studies should explore hybrid algorithms, advanced data structures, and multilingual datasets to further optimize performance and validate adaptability. Research Implications The enhanced algorithm provides a robust solution for real-time text processing, supporting industries that rely on efficient, large-scale analytics. Practical Implications Industries such as data analytics and content moderation can adopt this algorithm to meet growing demands for scalable and efficient computational tools. Social Implications Improved text-processing efficiency supports faster, more accurate content moderation, fostering safer and more respectful online environments.