Approximate String Matching Algorithm Research Articles

Mapping biological entities using the longest approximately common prefix method.

BackgroundThe significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction. The task of source integration in the Unified Medical Language System (UMLS) requires considerable expert effort despite the presence of various computational tools. This problem warrants the search for a new method for approximate string matching and its UMLS-based evaluation.ResultsThis paper introduces the Longest Approximately Common Prefix (LACP) method as an algorithm for approximate string matching that runs in linear time. We compare the LACP method for performance, precision and speed to nine other well-known string matching algorithms. As test data, we use two multiple-source samples from the Unified Medical Language System (UMLS) and two SNOMED Clinical Terms-based samples. In addition, we present a spell checker based on the LACP method.ConclusionsThe Longest Approximately Common Prefix method completes its string similarity evaluations in less time than all nine string similarity methods used for comparison. The Longest Approximately Common Prefix outperforms these nine approximate string matching methods in its Maximum F1 measure when evaluated on three out of the four datasets, and in its average precision on two of the four datasets.

Approximate Chinese String Matching Techniques Based on Pinyin Input Method

String matching is one of the most typical problems in computer science. Previous studies mainly focused on accurate string matching problem. However, with the rapid development of the computer and Internet as well as the continuously rising of new issues, people find that it has very important theoretical value and practical meaning to research and design efficient approximate string matching algorithms. Approximate string matching is also called string matching that allows errors, which mainly aims to find the pattern string in the text and database and allows k differences between the pattern string and its occurring forms in the text. For the problem of approximate string matching, though a number of algorithms have been proposed, there are fewer studies which focus on large size of alphabet . Most of experts are interested in small or middle size of alphabet . For large size of , especially for Chinese characters and Asian phonetics, there are fewer efficient algorithms. For the above reasons, this paper focuses on the approximate Chinese strings matching problem based on the pinyin input method.

An algorithm to improve the performance of string matching

Approximate string matching algorithms are techniques used to find a pattern ‘P’ in a text ‘T’ partially or exactly. These techniques become very important in terms of performance and the accuracy of searching results. In this paper, we propose a general approach algorithm, called the Direct Matching Algorithm (DMA). The function of this algorithm is to perform direct access matching for the exact pattern or its similarities within a text depending on the location of a character in alphabetical order. We simulated the DMA in order to show its competence. The simulation result showed significant improvement in the exact string matching or similarity matching, and therefore extreme competence in the real applications.

Fast algorithms for approximate circular string matching

BackgroundCircular string matching is a problem which naturally arises in many biological contexts. It consists in finding all occurrences of the rotations of a pattern of length m in a text of length n. There exist optimal average-case algorithms for exact circular string matching. Approximate circular string matching is a rather undeveloped area.ResultsIn this article, we present a suboptimal average-case algorithm for exact circular string matching requiring time O(n). Based on our solution for the exact case, we present two fast average-case algorithms for approximate circular string matching with k-mismatches, under the Hamming distance model, requiring time O(n) for moderate values of k, that is k=O(m/logm). We show how the same results can be easily obtained under the edit distance model. The presented algorithms are also implemented as library functions. Experimental results demonstrate that the functions provided in this library accelerate the computations by more than three orders of magnitude compared to a naïve approach.ConclusionsWe present two fast average-case algorithms for approximate circular string matching with k-mismatches; and show that they also perform very well in practice. The importance of our contribution is underlined by the fact that the provided functions may be seamlessly integrated into any biological pipeline. The source code of the library is freely available at http://www.inf.kcl.ac.uk/research/projects/asmf/.

Approximate Multiple Pattern String Matching using Bit Parallelism: A Review

String matching is to find all the occurrences of a given pattern in a large text both being sequence of characters drawn from finite alphabet set. Approximate String Matching involves the detection of correct patterns along with the detection of some wrong patterns inside the text. Bit Parallelism is a feature that can be used to detect patterns inside the text and is reported to result in more efficient approximate string matching. Bit parallelism enhances the processing speed of the approximate string matching algorithm as it takes the benefit of the internal bit operations taking place in parallel inside the system. The bit parallel method has also been compared with the traditional Aho Corasick Algorithms which consumes more time and memory. In general bit parallel are both memory and time efficient.

DNA 서열분석을 위한 거리합기반 문자열의 근사주기

주기와 같은 반복문자열에 대한 연구는 데이터압축, 컴퓨터활용 음악분석, 바이오인포매틱스 등 다양한 분야에서 진행되고 있다. 바이오인포매틱스 분야에서 주기는 유전자 서열이 반복적으로 나타나는 종렬중복과 밀접한 관련이 있으며 이는 근사문자열매칭을 이용한 근사주기 연구와 관련이 있다. 본 논문에서는 기존의 근사주기에 대한 정의를 보완하는 거리합기반 근사주기를 정의하고 이에 대한 연구 결과를 제시한다. 길이가 각각 m과 n인 문자열 p와 x가 주어졌을 때, p의 x에 대한 거리합기반 최소 근사주기거리를 가중편집거리에 대해 <TEX>$O(mn^2)$</TEX> 시간, 편집거리에 대해 O)(mn) 시간, 해밍거리에 대해 O(n) 시간에 계산하는 알고리즘을 제시한다. Repetitive strings such as periods have been studied vigorously in so diverse fields as data compression, computer-assisted music analysis, bioinformatics, and etc. In bioinformatics, periods are highly related to repetitive patterns in DNA sequences so called tandem repeats. In some cases, quite similar but not the same patterns are repeated and thus we need approximate string matching algorithms to study tandem repeats in DNA sequences. In this paper, we propose a new definition of approximate periods of strings based on distance sum. Given two strings <TEX>$p({\mid}p{\mid}=m)$</TEX> and <TEX>$x({\mid}x{\mid}=n)$</TEX>, we propose an algorithm that computes the minimum approximate period distance based on distance sum. Our algorithm runs in <TEX>$O(mn^2)$</TEX> time for the weighted edit distance, and runs in O(mn) time for the edit distance, and runs in O(n) time for the Hamming distance.

A Consensus Algorithm for Approximate String Matching

Approximate string matching (ASM) is a well-known computational problem with important applications in database searching, plagiarism detection, spelling correction, and bioinformatics. The two main issues with most ASM algorithms are (1) computational complexity, and (2) low specificity due to a large amount of false positives being reported. In this paper, a very efficient ASM method is proposed, along with a post -processing stage designed to significantly reduce the amount of false positives. Results with random strings show that the proposed method is capable of performing a search within a large (1M b) string in about 100ms, with a sensitivity and specificity of nearly 100%.

A BIT-PARALLEL DYNAMIC PROGRAMMING ALGORITHM SUITABLE FOR DNA SEQUENCE ALIGNMENT

Myers' elegant and powerful bit-parallel dynamic programming algorithm for approximate string matching has a restriction that the query length should be within the word size of the computer, typically 64. We propose a modification of Myers' algorithm, in which the modification has a restriction not on the query length but on the maximum number of mismatches (substitutions, insertions, or deletions), which should be less than half of the word size. The time complexity is O(m log |Σ|), where m is the query length and |Σ| is the size of the alphabet Σ. Thus, it is particularly suited for sequences on a small alphabet such as DNA sequences. In particular, it is useful in quickly extending a large number of seed alignments against a reference genome for high-throughput short-read data produced by next-generation DNA sequencers.

Fast Algorithms for Top-k Approximate String Matching

Top-k approximate querying on string collections is an important data analysis tool for many applications, and it has been exhaustively studied. However, the scale of the problem has increased dramatically because of the prevalence of the Web. In this paper, we aim to explore the efficient top-k similar string matching problem. Several efficient strategies are introduced, such as length aware and adaptive q-gram selection. We present a general q-gram based framework and propose two efficient algorithms based on the strategies introduced. Our techniques are experimentally evaluated on three real data sets and show a superior performance.

Approximate String Matching with Compressed Indexes

A compressed full-text self-index for a text T is a data structure requiring reduced space and able to search for patterns P in T. It can also reproduce any substring of T, thus actually replacing T. Despite the recent explosion of interest on compressed indexes, there has not been much progress on functionalities beyond the basic exact search. In this paper we focus on indexed approximate string matching (ASM), which is of great interest, say, in bioinformatics. We study ASM algorithms for Lempel-Ziv compressed indexes and for compressed suffix trees/arrays. Most compressed self-indexes belong to one of these classes. We start by adapting the classical method of partitioning into exact search to self-indexes, and optimize it over a representative of either class of self-index. Then, we show that a Lempel- Ziv index can be seen as an extension of the classical q-samples index. We give new insights on this type of index, which can be of independent interest, and then apply them to a Lempel- Ziv index. Finally, we improve hierarchical verification, a successful technique for sequential searching, so as to extend the matches of pattern pieces to the left or right. Most compressed suffix trees/arrays support the required bidirectionality, thus enabling the implementation of the improved technique. In turn, the improved verification largely reduces the accesses to the text, which are expensive in self-indexes. We show experimentally that our algorithms are competitive and provide useful space-time tradeoffs compared to classical indexes.

Identification of design motifs with pattern matching algorithms

Design patterns are important in software maintenance because they help in understanding and re-engineering systems. They propose design motifs, solutions to recurring design problems. The identification of occurrences of design motifs in large systems consists of identifying classes whose structure and organization match exactly or approximately the structure and organization of classes as suggested by the motif. We adapt two classical approximate string matching algorithms based on automata simulation and bit-vector processing to efficiently identify exact and approximate occurrences of motifs. We then carry out two case studies to show the performance, precision, and recall of our algorithms. In the first case study, we assess the performance of our algorithms on seven medium-to-large systems. In the second case study, we compare our approach with three existing approaches (an explanation-based constraint approach, a metric-enhanced explanation-based constraint approach, and a similarity scoring approach) by applying the algorithms on three small-to-medium size systems, JH otD raw, J uzzle, and Q uickUML. Our studies show that approximate string matching based on bit-vector processing provides efficient algorithms to identify design motifs.

Approximate Boyer-Moore String Matching for Small Alphabets

Recently a new variation of approximate Boyer-Moore string matching was presented for the k-mismatch problem. The variation, called FAAST, is specifically tuned for small alphabets. We further improve this algorithm gaining speedups in both preprocessing and searching. We also present three variations of the algorithm for the k-difference problem. We show that the searching time of the algorithms is average-optimal and the preprocessing also has a lower time complexity than FAAST. Our experiments show that our algorithm for the k-mismatch problem is about 30% faster than FAAST and the new algorithms compare well against other state-of-the-art algorithms for approximate string matching.

Improved algorithms for approximate string matching (extended abstract)

BackgroundThe problem of approximate string matching is important in many different areas such as computational biology, text processing and pattern recognition. A great effort has been made to design efficient algorithms addressing several variants of the problem, including comparison of two strings, approximate pattern identification in a string or calculation of the longest common subsequence that two strings share.ResultsWe designed an output sensitive algorithm solving the edit distance problem between two strings of lengths n and m respectively in time O((s - |n - m|)·min(m, n, s) + m + n) and linear space, where s is the edit distance between the two strings. This worst-case time bound sets the quadratic factor of the algorithm independent of the longest string length and improves existing theoretical bounds for this problem. The implementation of our algorithm also excels in practice, especially in cases where the two strings compared differ significantly in length.ConclusionWe have provided the design, analysis and implementation of a new algorithm for calculating the edit distance of two strings with both theoretical and practical implications. Source code of our algorithm is available online.

Efficient and effectiveness retrieval of information using some of the approximate string matching algorithms

Efficient and effectiveness retrieval of information using some of the approximate string matching algorithms

Improving the bit-parallel NFA of Baeza-Yates and Navarro for approximate string matching

We propose a new variant of the bit-parallel NFA of Baeza-Yates and Navarro (BPD) for approximate string matching [R. Baeza-Yates, G. Navarro, Faster approximate string matching, Algorithmica 23 (1999) 127–158]. BPD is one of the most practical approximate string matching algorithms under moderate pattern lengths and error levels [G. Myers, A fast bit-vector algorithm for approximate string matching based on dynamic programming, J. ACM 46 (3) 1989 395–415; G. Navarro, M. Raffinot, Flexible Pattern Matching in Strings—Practical On-line Search Algorithms for Texts and Biological Sequences, Cambridge University Press, Cambridge, UK, 2002]. Given a length- m pattern and an error threshold k, the original BPD requires ( m − k ) ( k + 2 ) bits of space to represent an NFA with ( m − k ) ( k + 1 ) states. In this paper we remove redundancy from the original NFA representation. Our variant requires ( m − k ) ( k + 1 ) bits of space, which is optimal in the sense that exactly one bit per state is used. The space efficiency is achieved by using an alternative, but equally or even more efficient, simulation algorithm for the bit-parallel NFA. We also present experimental results to compare our modified NFA against the original BPD and its main competitors. Our new variant is more efficient than the original BPD, and it hence takes over/extends the role of the original BPD as one of the most practical approximate string matching algorithms under moderate values of k and m.

Near optimal multiple alignment within a band in polynomial time

Multiple sequence alignment is a fundamental problem in computational biology. Because of its notorious difficulties, aligning sequences within a constant band ( c-diagonal) is a popular practice in bioinformatics with good practical results [D. Sankoff, J. Kruskal, Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, Addison–Wesley, 1983; W.R. Pearson, D. Lipman, Improved tools for biological sequence comparison, Proc. Natl. Acad. Sci. USA 85 (1988) 2444–2448; W.R. Pearson, Rapid and sensitive sequence comparison with FASTP and FASTA, Methods Enzymol. 183 (1990) 63–98; W.R. Pearson, Searching protein sequence libraries: Comparison of the sensitivity and selectivity of the Smith–Waterman and FASTA algorithms, Genomics 11 (1991) 635–650; S. Altschul, D. Lipman, Trees, stars, and multiple sequence alignment, SIAM J. Appl. Math. 49 (1989) 197–209; K. Chao, W.R. Pearson, W. Miller, Aligning two sequences within a specified diagonal band, CABIOS 8 (1992) 481–487; J.W. Fickett, Fast optimal alignment, Nucleic Acids Res. 12 (1984) 175–180; E. Ukkonen, Algorithms for approximate string matching, Inform. Control 64 (1985) 100–118; J.L. Spouge, Fast optimal alignment, CABIOS 7 (1991) 1–7]. However, the problem is still NP-hard for multiple sequences. In this paper, we present a theoretical study of this problem. In particular, for arbitrarily small ϵ > 0 , we present polynomial time algorithms that produce a multiple alignment (not necessarily c-diagonal) with cost at most 1 + ϵ times the cost of the optimal c-diagonal alignment, under standard models of both SP alignment and consensus (star) alignment. Our algorithms for consensus alignment allow very general score schemes. In order to prove our main results, we also present similar results for SP alignment and consensus alignment, allowing only constant number of insertion and deletion gaps (of arbitrary length) per sequence on the average. These results are interesting in their own rights and they improve some results in [M. Li, B. Ma, L. Wang, Finding similar regions in many sequences, in: Proc. 31st ACM Symp. Theory of Computing, Atlanta, 1999, pp. 473–482].

A programmable array processor architecture for flexible approximate string matching algorithms

Approximate string matching problem is a common and often repeated task in information retrieval and bioinformatics. This paper proposes a generic design of a programmable array processor architecture for a wide variety of approximate string matching algorithms to gain high performance at low cost. Further, we describe the architecture of the array and the architecture of the cell in detail in order to efficiently implement for both the preprocessing and searching phases of most string matching algorithms. Further, the architecture performs approximate string matching for complex patterns that contain don’t care, complement and classes symbols. We also simulate and evaluate the proposed architecture on a field programmable gate array (FPGA) device using the JHDL tool for synthesis and the Xilinx Foundation tools for mapping, placement, and routing. Finally, our programmable implementation achieves about 8–340 times faster execution than a desktop computer with a Pentium 4 3.5 GHz for all algorithms when the length of the pattern is 1024.

Introduction of New Associate Editors

The EiC and Associate EiC express our gratitude to David Forsyth, Brendan Frey, Venu Govindaraju, and Cordelia Schmid who are retiring as associate editors of TPAMI. While we will miss their dedication to the transactions, we hope that they will be enjoying a bit more free time. We are also pleased to announce that Professor Daniel Lopresti, Professor B.S. Manjunath, Professor Marc Pollefeys, and Professor Ramin Zabih have joined the editorial board. Professor Lopresti will oversee papers in document and handwriting analysis, biometrics, approximate string matching algorithms, and performance evaluation. Professor Manjunath will be considering papers in feature extraction, segmentation, image/video retrieval, and image registration. Professor Pollefeys will be responsible for submissions in structure from motion, stereo, multiple view geometry and camera calibration, 3D and appearance modeling, shape-from-X techniques, and novel sensors. Professor Zabih will handle papers on stereo and medical imaging as well as energy minimization and graph algorithms. We look forward to working with them. Their brief biographies appear herein.

Introduction of New Associate Editors

The EiC and Associate EiC express our gratitude to David Forsyth, Brendan Frey, Venu Govindaraju, and Cordelia Schmid who are retiring as associate editors of TPAMI. While we will miss their dedication to the transactions, we hope that they will be enjoying a bit more free time. We are also pleased to announce that Professor Daniel Lopresti, Professor B.S. Manjunath, Professor Marc Pollefeys, and Professor Ramin Zabih have joined the editorial board. Professor Lopresti will oversee papers in document and handwriting analysis, biometrics, approximate string matching algorithms, and performance evaluation. Professor Manjunath will be considering papers in feature extraction, segmentation, image/video retrieval, and image registration. Professor Pollefeys will be responsible for submissions in structure from motion, stereo, multiple view geometry and camera calibration, 3D and appearance modeling, shape-from-X techniques, and novel sensors. Professor Zabih will handle papers on stereo and medical imaging as well as energy minimization and graph algorithms. We look forward to working with them. Their brief biographies appear herein.

A software system for gene sequence database construction based on fast approximate string matching

We propose a web-based software system for sequence acquisition and database construction. An example application of this system is to construct a ribosomal RNA gene (rDNA) sequence database to facilitate the study of microbial communities. A fast and accurate approximate string matching algorithm is implemented to fetch rDNA sequences sandwiched by two given primers from GenBank. A homology search algorithm based on Basic-Local-Alignment-Search-Tool (BLAST) is then used to extract rDNA sequences that do not contain the primers. This two step process leads to an rDNA sequence database for a specific taxonomic group. We consider the distance between the occurrences of the two given primers, mismatches and degeneracy when performing string matching. In the homology search, a chaining algorithm is combined with BLAST to obtain global alignments based on local alignments. This system can be used in many biological applications.