A Compressed Enhanced Suffix Array Supporting Fast String Matching

2009 ◽  
pp. 51-62 ◽  
Author(s):  
Enno Ohlebusch ◽  
Simon Gog
Keyword(s):  
String Matching ◽  
Suffix Array

An Efficient Algorithm for Suffix Sorting

2016 ◽  
Vol 30 (06) ◽  
pp. 1659018 ◽  
Author(s):  
Zhan Peng ◽  
Yuping Wang ◽  
Xingsi Xue ◽  
Jingxuan Wei
Keyword(s):  
Data Structure ◽  
Efficient Algorithm ◽  
String Matching ◽  
Suffix Array ◽  
Experimental Results ◽  
Sorting Algorithm ◽  
Lexicographical Order ◽  
Sorting Algorithms ◽  
Primary Problem

The Suffix Array (SA) is a fundamental data structure which is widely used in the applications such as string matching, text index and computation biology, etc. How to sort the suffixes of a string in lexicographical order is a primary problem in constructing SAs, and one of the widely used suffix sorting algorithms is qsufsort. However, qsufsort suffers one critical limitation that the order of suffixes starting with the same [Formula: see text] characters cannot be determined in the kth round. To this point, in our paper, an efficient suffix sorting algorithm called dsufsort is proposed by overcoming the drawback of the qsufsort algorithm. In particular, our proposal maintains the depth of each unsorted portion of SA, and sorts the suffixes based on the depth in each round. By this means, some suffixes that cannot be sorted by qsufsort in each round can be sorted now, as a result, more sorting results in current round can be utilized by the latter rounds and the total number of sorting rounds will be reduced, which means dsufsort is more efficient than qsufsort. The experimental results show the effectiveness of the proposed algorithm, especially for the text with high repetitions.

An Efficient Tool for Searching Maximal and Super Maximal Repeats in Large DNA/Protein Sequences via Induced-Enhanced Suffix Array

2019 ◽  
Vol 12 (2) ◽  
pp. 128-134
Author(s):  
Sanjeev Kumar ◽  
Suneeta Agarwal ◽  
Ranvijay
Keyword(s):  
Protein Sequences ◽  
Input Sequence ◽  
Suffix Array ◽  
Secondary Memory ◽  
Time And Space ◽  
Efficient Tool ◽  
Frequency Distributions ◽  
Alignment Algorithms ◽  
Common Prefix ◽  
Art Works

Background: DNA and Protein sequences of an organism contain a variety of repeated structures of various types. These repeated structures play an important role in Molecular biology as they are related to genetic backgrounds of inherited diseases. They also serve as a marker for DNA mapping and DNA fingerprinting. Efficient searching of maximal and super maximal repeats in DNA/Protein sequences can lead to many other applications in the area of genomics. Moreover, these repeats can also be used for identification of critical diseases by finding the similarity between frequency distributions of repeats in viruses and genomes (without using alignment algorithms). Objective: The study aims to develop an efficient tool for searching maximal and super maximal repeats in large DNA/Protein sequences. Methods: The proposed tool uses a newly introduced data structure Induced Enhanced Suffix Array (IESA). IESA is an extension of enhanced suffix array. It uses induced suffix array instead of classical suffix array. IESA consists of Induced Suffix Array (ISA) and an additional array-Longest Common Prefix (LCP) array. ISA is an array of all sorted suffixes of the input sequence while LCP array stores the lengths of the longest common prefixes between all pairs of consecutive suffixes in an induced suffix array. IESA is known to be efficient w.r.t. both time and space. It facilitates the use of secondary memory for constructing the large suffix-array. Results: An open source standalone tool named MSR-IESA for searching maximal and super maximal repeats in DNA/Protein sequences is provided at https://github.com/sanjeevalg/MSRIESA. Experimental results show that the proposed algorithm outperforms other state of the art works w.r.t. to both time and space. Conclusion: The proposed tool MSR-IESA is remarkably efficient for the analysis of DNA/Protein sequences, having maximal and super maximal repeats of any length. It can be used for identification of well-known diseases.

Accelerating Knuth-Morris-Pratt String Matching over LZ77 Compressed Text

2021 ◽  
Author(s):  
Xiuwen Sun ◽  
Di Wu ◽  
Da Mo ◽  
Jie Cui ◽  
Hong Zhong
Keyword(s):  
String Matching

scholarly journals Contribution to Semantic Analysis of Arabic Language

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Anis Zouaghi ◽  
Mounir Zrigui ◽  
Georges Antoniadis ◽  
Laroussi Merhbene
Keyword(s):  
Information Retrieval ◽  
Semantic Analysis ◽  
String Matching ◽  
Ambiguous Word ◽  
Arabic Language ◽  
New Approach ◽  
Matching Algorithm ◽  
Ambiguous Words ◽  
Lesk Algorithm ◽  
Context Of Use

We propose a new approach for determining the adequate sense of Arabic words. For that, we propose an algorithm based on information retrieval measures to identify the context of use that is the closest to the sentence containing the word to be disambiguated. The contexts of use represent a set of sentences that indicates a particular sense of the ambiguous word. These contexts are generated using the words that define the senses of the ambiguous words, the exact string-matching algorithm, and the corpus. We use the measures employed in the domain of information retrieval, Harman, Croft, and Okapi combined to the Lesk algorithm, to assign the correct sense of those proposed.

Suffix array for multi-pattern matching with variable length wildcards

2021 ◽  
Vol 25 (2) ◽  
pp. 283-303
Author(s):  
Na Liu ◽  
Fei Xie ◽  
Xindong Wu
Keyword(s):  
Dynamic Programming ◽  
Data Structure ◽  
Pattern Matching ◽  
Edit Distance ◽  
State Of The Art ◽  
Suffix Array ◽  
Variable Length ◽  
Distance Method ◽  
Efficient Data ◽  
Comparison Algorithms

Approximate multi-pattern matching is an important issue that is widely and frequently utilized, when the pattern contains variable-length wildcards. In this paper, two suffix array-based algorithms have been proposed to solve this problem. Suffix array is an efficient data structure for exact string matching in existing studies, as well as for approximate pattern matching and multi-pattern matching. An algorithm called MMSA-S is for the short exact characters in a pattern by dynamic programming, while another algorithm called MMSA-L deals with the long exact characters by the edit distance method. Experimental results of Pizza & Chili corpus demonstrate that these two newly proposed algorithms, in most cases, are more time-efficient than the state-of-the-art comparison algorithms.

scholarly journals Entropy-Based Approach in Selection Exact String-Matching Algorithms

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 31
Author(s):  
Ivan Markić ◽  
Maja Štula ◽  
Marija Zorić ◽  
Darko Stipaničev
Keyword(s):  
String Matching ◽  
Quantitative Measure ◽  
Search Pattern ◽  
Maximum Efficiency ◽  
Resource Usage ◽  
Specific Domain ◽  
Matching Problems ◽  
Algorithm Efficiency ◽  
Testing Algorithm ◽  
Algorithm Implementation

The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.

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