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"Exact Packed String Matching" optimized for SIMD SSE4.2 (x86_64 and aarch64). It performs stable and best on all sizes. The site I linked to compares 199 fast string search algorithms, with the usual ones (BM, KMP, BMH) being pretty slow. EPSM outperforms all the others being mentioned here on these platforms. It's also the latest.
Now when you get a single word search term, you can walk the trie and get the list of matches for that word easily. If you get a multiple word search term, you can do the following. Walk the trie with the first word in the search term. Get the list of matches and insert into a hashTable H1. Now walk the trie with the second word in the search term.
Instead, you could try each algorithm with different string sizes and see how the algorithm behaves as string size varies. And Mark's advice is good too. So you are running repeated trials for many different string lengths to get a picture of how one algorithm works, then repeating that for the next algorithm.
you can use BM algorithm for search in text files for single pattern, and repeat this algorithm for all the patterns you have in your list. the other best solution is to use multi-pattern search algorithms like: Aho–Corasick string matching algorithm
You're confusing fuzzy search algorithms with implementation: a fuzzy search of a word may return 400 results of all the words that have Levenshtein distance of, say, 2. But, to the user you have to display only the top 5-10. Implementation-wise, you'll pre-process all the words in the dictionary and save the results into a DB.
The search begins at startPos because the target area is somewhat removed from the start; It loops through the string, at each step it checks if the substring from that point starts with the startMatchString, which is an indicator that the start of the target string has been found. (The length of the target string varys).
If the input is not too large, you don't want to repeat the search many times and you do not have many patterns, it might be a good idea to use a single pattern algorithm several times. The Wikipedia article on search algorithms gives many algorithms with running and preprocessing times. Implementations:
KMP is better if you only have to search for one string in another single string, and not have to do it a lot of times. A suffix tree is a much more general data structure, so you can do a lot more with it. See what you can do with it here. KMP is useful for finding if a string is a substring in another string.
There are many very good algorithms for doing exact string matching. Given a query string of length 200, and a target string of length 3 billion (the human genome), we want to find any place in the target where there is a substring of length k that matches a substring of the query exactly.
Furthermore, string searching algorithms are exceptions since there is Boyer-Moore which is so nice (all pro no con) that is deemed standard benchmark. What I mean to say is that usually choosing an algorithm for a library can be really hard, but with strings I would not expect anything less to be used in any library worth anything.