scholarly journals On the computational complexity of 2-interval pattern matching problems

2004 ◽  
Vol 312 (2-3) ◽  
pp. 223-249 ◽  
Author(s):  
Stéphane Vialette
Keyword(s):  
Computational Complexity ◽  
Pattern Matching ◽  
Matching Problems

scholarly journals A Parameterized Study of Maximum Generalized Pattern Matching Problems

Algorithmica ◽  
2015 ◽  
Vol 75 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Sebastian Ordyniak ◽  
Alexandru Popa
Keyword(s):  
Pattern Matching ◽  
Matching Problems ◽  
Generalized Pattern Matching

STRING MATCHING ARTIFICIAL NEURAL NETWORKS

2001 ◽  
Vol 11 (05) ◽  
pp. 445-453 ◽  
Author(s):  
TATIANA TAMBOURATZIS
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Complexity ◽  
Building Block ◽  
String Matching ◽  
Matching Problems ◽  
Low Computational Complexity ◽  
Artificial Neural ◽  
Harmony Theory ◽  
Fast Match

Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity, and activation values of the ANN output nodes that can be used as indicators of substitution, insertion (addition) and deletion spelling errors.

A Criteria Transformation Approach to Pattern Matching based on Non-Linear Parameter Optimization

2015 ◽  
Vol 24 (2) ◽  
pp. 249-263 ◽  
Author(s):  
Christian John ◽  
Dietmar Tutsch ◽  
Thomas Lepich ◽  
Bernard Beitz ◽  
Reinhard Möller
Keyword(s):  
Pattern Matching ◽  
Parameter Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Object Oriented Programming ◽  
Information Modeling ◽  
Linear Parameter ◽  
Matching Problems ◽  
Wide Range ◽  
Non Linear

AbstractThis paper presents a concept for pattern matching based on a parameter optimization system for approximative numerical calculation of some parameter combination under soft and hard constraints. The concept uses a non-linear parameter optimization method with an iterative variation of parameters. The paper focuses on the information modeling process to migrate problem-domain specific criteria into optimization-compatible objects suitable for a standardized parameter optimization procedure. A step-by-step transformation process is presented and implemented in object-oriented programming: classes and interfaces. The method is applicable to a wide range of pattern-matching problems due to its flexibility and extensibility.

A Fast Engine for Multi-String Pattern Matching

2017 ◽  
Vol 31 (12) ◽  
pp. 1750039 ◽  
Author(s):  
Zhan Peng ◽  
Yuping Wang ◽  
Wei Yue
Keyword(s):  
Data Structure ◽  
Pattern Matching ◽  
Large Scale ◽  
String Matching ◽  
Memory Consumption ◽  
Matching Problems ◽  
Text String ◽  
Tree Node ◽  
String Pattern ◽  
Better Than

Multi-string matching (MSM) is a core technique searching a text string for all occurrences of some string patterns. It is widely used in many applications. However, as the number of string patterns increases, most of the existing algorithms suffer from two issues: the long matching time, and the high memory consumption. To address these issues, in this paper, a fast matching engine is proposed for large-scale string matching problems. Our engine includes a filter module and a verification module. The filter module is based on several bitmaps which are responsible for quickly filtering out the invalid positions in the text, while for each potential matched position, the verification module confirms true pattern occurrence. In particular, we design a compact data structure called Adaptive Matching Tree (AMT) for the verification module, in which each tree node only saves some pattern fragments of the whole pattern set and the inner structure of each tree node is chosen adaptively according to the features of the corresponding pattern fragments. This makes the engine time and space efficient. The experiments indicate that, our matching engine performs better than the compared algorithms, especially for large pattern sets.

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