A bit-parallel approach to suffix automata: Fast extended string matching

1998 ◽  
pp. 14-33 ◽  
Author(s):  
Gonzalo Navarro ◽  
Mathieu Raffinot
Keyword(s):  
String Matching

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

A two-party private string matching fuzzy vault scheme

2021 ◽  
Author(s):  
Xhino Mullaymeri ◽  
Alexandros Karakasidis
Keyword(s):  
String Matching ◽  
Fuzzy Vault

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.

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.

A constant-time optimal parallel string-matching algorithm

1995 ◽  
Vol 42 (4) ◽  
pp. 908-918 ◽  
Author(s):  
Zvi Galil
Keyword(s):  
String Matching ◽  
Constant Time ◽  
Matching Algorithm ◽  
Time Optimal ◽  
Parallel String Matching

scholarly journals Multipattern string matching with q -grams

2007 ◽  
Vol 11 ◽  
Author(s):  
Leena Salmela ◽  
Jorma Tarhio ◽  
Jari Kytöjoki
Keyword(s):  
String Matching
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