Optimizing Big Data Pattern Search: Advanced String Manipulation Techniques in C++

This research conducts a comprehensive exploration and comparison of various string pattern matching algorithms, including Knuth-Morris-Pratt (KMP), Aho-Corasick, Finite Automata, Boyer-Moore, and Rabin-Karp.Our primary focus is on evaluating their performance with big data.As the size of the data increases, the need for efficient pattern matching becomes critical.We analyze each algorithm's strengths and weaknesses to identify the most effective approach for handling large-scale datasets.Additionally, we meticulously evaluate vital metrics such as runtime, memory utilization, and overall computational efficacy.These findings provide valuable insights for selecting the optimal method and simplifying the management of extensive pattern-matching tasks.Our contributions include comprehensive performance evaluation, scalability analysis, memory utilization insights, a balanced view of computational efficiency, real-world applicability, trade-off analysis, and actionable recommendations.