An Optimized Pattern Matching Algorithm Based on Suffix Arrays with LCP-Guided Binary Search: A Time-Efficient Approach for Large-Scale Text Processing

Pattern matching is a fundamental operation in computational biology, information retrieval, and text processing. Despite advances in algorithms, efficiently searching large-scale texts, particularly genomic sequences spanning billions of base pairs, remains a significant challenge. Existing approaches face substantial limitations including Boyer-Moore algorithm's poor worst-case performance of O(mn) and limited scalability on large datasets, KMP algorithm's high memory overhead and poor cache locality despite O(m+n) complexity, traditional suffix array methods' expensive O(mlogn) search time with costly preprocessing, and hash-based approaches like Rabin-Karp's vulnerability to hash collisions and poor performance on repetitive sequences common in genomic data. This paper presents LCP-Optimized Suffix Array (LOSA), a high-performance pattern matching algorithm that combines suffix arrays with Longest Common Prefix (LCP) array optimizations and parallel processing to achieve faster search times with scalable memory usage. LOSA reduces the worst-case search complexity from $O(m \log n)$ to nearly $O(m+\log n)$, where m length pattern and n length the text. This improvement is achieved through LCP-aware skips to minimize redundant comparisons, as well as parallelized construction and query phases, for modern multicore systems.