Approximate string matching

About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.

Locating Maximal Multirepeats in Multiple Strings Under Various Constraints†A preliminary version of the results of this paper was presented in CPM 2002.

Abstract: A multirepeat in a string is a substring (factor) that appears a predefined number of times. A multirepeat is maximal if it cannot be extended either to the right or to the left and produce a multirepeat. In this paper, we present algorithms for two different versions of the problem of finding maximal multirepeats in a set of strings. In the case of arbitrary gaps, we propose an algorithm with O(σN2n + α) time complexity. When the gap is bounded in a small range c, we propose an algorithm with O((c2 + σ2)mN2n log(Nn) + α) time complexity. Here, N is the number of strings, n the mean length of each string, m the multiplicity of the multirepeat and α the number of reported occurrences. Our results extend previous work by considering sets of strings as well as by generalizing pairs to multirepeats.

Bit-parallel approximate string matching algorithms with transposition

Abstract: Using bit-parallelism has resulted in fast and practical algorithms for approximate string matching under the Levenshtein edit distance, which permits a single edit operation to insert, delete or substitute a character. Depending on the parameters of the search, currently the fastest non-filtering algorithms in practice are the O(kn[m/ω]) algorithm of Wu & Manber, the O([km/ω]n) algorithm of Baeza-Yates & Navarro, and the O([m/ω]n) algorithm of Myers, where m is the pattern length, n is the text length, k is the error threshold and w is the computer word size. In this paper we discuss a uniform way of modifying each of these algorithms to permit also a fourth type of edit operation: transposing two adjacent characters in the pattern. This type of edit distance is also known as the Damerau edit distance. In the end we also present an experimental comparison of the resulting algorithms.

Implementation and performance evaluation of fuzzy file block matching

Abstract: The fuzzy file block matching technique (fuzzy matching for short), was first proposed for opportunistic use of Content Addressable Storage. Fuzzy matching aims to increase the hit ratio in the content-addressable storage providers, and thus can improve the performance of underlying distributed file storage systems by potentially saving significant network bandwidth and reducing file transmission costs. Fuzzy matching employs shingling to represent the fuzzy hashing of file blocks for similarity detection, and error-correcting information to reconstruct the canonical content of a file block from some similar blocks. In this paper, we present the implementation details of fuzzy matching and a very basic evaluation of its performance. In particular, we show that fuzzy matching can recover new versions of GNU Emacs source from older versions.

Nested Counters in Bit-Parallel String Matching

Abstract: Many algorithms, e.g. in the field of string matching, are based on handling many counters, which can be performed in parallel, even on a sequential machine, using bit-parallelism. The recently presented technique of nested counters (Matryoshka counters ) [1] is to handle small counters most of the time, and refer to larger counters periodically, when the small counters may get full, to prevent overflow. In this work, we present several non-trivial applications of Matryoshka counters in string matching algorithms, improving their worst- or average-case time complexities. The set of problems comprises (Δ ,α )-matching, matching with k insertions, episode matching, and matching under Levenshtein distance.