Speeding Up String Matching by Weak Factor Recognition
This work addresses efficiency in string matching for applications in computer science, though it is incremental as it builds on existing factor recognition techniques.
The paper tackles the problem of string matching by introducing an algorithm based on weak factor recognition and hashing, which achieves up to a 28% speed improvement in running times for small alphabets and long patterns compared to existing methods.
String matching is the problem of finding all the substrings of a text which match a given pattern. It is one of the most investigated problems in computer science, mainly due to its very diverse applications in several fields. Recently, much research in the string matching field has focused on the efficiency and flexibility of the searching procedure and quite effective techniques have been proposed for speeding up the existing solutions. In this context, algorithms based on factors recognition are among the best solutions. In this paper, we present a simple and very efficient algorithm for string matching based on a weak factor recognition and hashing. Our algorithm has a quadratic worst-case running time. However, despite its quadratic complexity, experimental results show that our algorithm obtains in most cases the best running times when compared, under various conditions, against the most effective algorithms present in literature. In the case of small alphabets and long patterns, the gain in running times reaches 28%. This makes our proposed algorithm one of the most flexible solutions in practical cases.