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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.


Papers
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Patent
13 Jun 2007
TL;DR: A computer-implemented technique for fuzzy matching is described in this article, which works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computerreadable records.
Abstract: A computer-implemented technique for fuzzy matching. This works quickly yet accurately to determine if a given computer-readable record is represented, by exact match or pretty close match, in a large collection of computer-readable records. Further tools may be provided to assess the character of the match.

23 citations

Journal ArticleDOI
TL;DR: This paper presents an algorithm running in O(nNlg(N/n) time for computing the edit-distance of these two strings under any rational scoring function, and an O( n2/3N4/3) time algorithm for arbitrary scoring functions.
Abstract: The edit distance problem is a classical fundamental problem in computer science in general, and in combinatorial pattern matching in particular. The standard dynamic programming solution for this problem computes the edit-distance between a pair of strings of total length O(N) in O(N2) time. To this date, this quadratic upper-bound has never been substantially improved for general strings. However, there are known techniques for breaking this bound in case the strings are known to compress well under a particular compression scheme. The basic idea is to first compress the strings, and then to compute the edit distance between the compressed strings. As it turns out, practically all known o(N2) edit-distance algorithms work, in some sense, under the same paradigm described above. It is therefore natural to ask whether there is a single edit-distance algorithm that works for strings which are compressed under any compression scheme. A rephrasing of this question is to ask whether a single algorithm can exploit the compressibility properties of strings under any compression method, even if each string is compressed using a different compression. In this paper we set out to answer this question by using straight line programs. These provide a generic platform for representing many popular compression schemes including the LZ-family, Run-Length Encoding, Byte-Pair Encoding, and dictionary methods. For two strings of total length N having straight-line program representations of total size n, we present an algorithm running in O(nNlg(N/n)) time for computing the edit-distance of these two strings under any rational scoring function, and an O(n2/3N4/3) time algorithm for arbitrary scoring functions. Our new result, while providing a speed up for compressible strings, does not surpass the quadratic time bound even in the worst case scenario.

23 citations

Proceedings ArticleDOI
Koji Nakano1
05 Dec 2012
TL;DR: This paper shows efficient implementations of approximate string matching on the memory machine models DMM and UMM for strings X and Y with length m and n, respectively.
Abstract: The Discrete Memory Machine (DMM) and the Unified Memory Machine (UMM) are theoretical parallel computing models that capture the essence of the shared memory access and the global memory access of GPUs The approximate string matching for two strings $X$ and $Y$ is a task to find a sub string of $Y$ most similar to $X$ The main contribution of this paper is to show efficient implementations of approximate string matching on the memory machine models Our best implementation for strings $X$ and $Y$ with length $m$ and $n$ ($m\leq n$), respectively, runs in $O({mn\over w}+ml)$ time units using $n$ threads both on the DMM on the UMM with width $w$ and latency $l$

23 citations

Book ChapterDOI
07 Oct 2017
TL;DR: This paper proposes an index-based method that relies on a filter-and-verify framework to support efficient Jaro-Winkler distance similarity search on a large dataset and leverages e-variants methods to build the index structure and pigeonhole principle to perform the search.
Abstract: Jaro-Winkler distance is a measurement to measure the similarity between two strings. Since Jaro-Winkler distance performs well in matching personal and entity names, it is widely used in the areas of record linkage, entity linking, information extraction. Given a query string q, Jaro-Winkler distance similarity search finds all strings in a dataset D whose Jaro-Winkler distance similarity with q is no more than a given threshold \(\tau \). With the growth of the dataset size, to efficiently perform Jaro-Winkler distance similarity search becomes challenge problem. In this paper, we propose an index-based method that relies on a filter-and-verify framework to support efficient Jaro-Winkler distance similarity search on a large dataset. We leverage e-variants methods to build the index structure and pigeonhole principle to perform the search. The experiment results clearly demonstrate the efficiency of our methods.

23 citations

Journal Article
TL;DR: This paper proposes and algorithm with finds the minimum distance t such that P is a t-approximate cover of T, which is an approximate version of covers.
Abstract: Repetitive strings have been studied in such diverse fields as molecular biology data compression etc. Some important regularities that have been studied are perods, covers seeds and squares. A natural extension of the repetition problems is to allow errors. Among the four notions above aproximate squares and approximate periodes have been studied. In this paper, we introduce the notion of approximate covers which is an approximate version of covers. Given two strings P(|P|=m) and T(|T|=n) we propose and algorithm with finds the minimum distance t such that P is a t-approximate cover of T. The algorithm take O(m,n) time for the edit distance and time of finding a string which is an approximate cover of T is minimum distance is NP-complete.

23 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202230
202132
202030
201948
201839