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.

Method and system for searching indexed string containing a search string

Abstract: Systems and methods for indexing and searching the inner structure of a string over a language having a vocabulary and a grammar using bit vectors. The index preserves the inner grammatical structure of the string while allowing for a fast search. A single search provides immediate access to every level of a document, without having to re-search a single string to determine which sub-parts of that string match the search string. When a string is indexed, the index maintains a compositional representation and the grammatical relationship between the elements of the vocabulary according to the language. The string is then indexed on different levels by disregarding some of the grammatical relationships of component levels.

On the closest string via rank distance

Abstract: Given a set S of k strings of maximum length n, the goal of the closest substring problem (CSSP) is to find the smallest integer d (and a corresponding string t of length l≤n) such that each string s∈S has a substring of length l of "distance" at most d to t. The closest string problem (CSP) is a special case of CSSP where l=n. CSP and CSSP arise in many applications in bioinformatics and are extensively studied in the context of Hamming and edit distance. In this paper we consider a recently introduced distance measure, namely the rank distance. First, we show that the CSP and CSSP via rank distance are NP-hard. Then, we present a polynomial time k-approximation algorithm for the CSP problem. Finally, we give a parametrized algorithm for the CSP (the parameter is the number of input strings) if the alphabet is binary and each string has the same number of 0's and 1's.

Swiftly Computing Center Strings

Abstract: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete. In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is effecient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application. We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.