Single Pattern Matching Research Articles

Design and Analysis of Pattern Matching Algorithms Based on QuRAM Processing

The pattern matching problem remains in survival since past decades and becomes more sophisticated due to exponential increase in size of text databases. An effective deterministic classical algorithm is always expected to be at least $${\rm O}\left( N \right)$$ time. Quantum computations are enough capable of performing exponential operations in single step of execution, so the quantum algorithms are effective. In general, the quantum pattern matching solution is possible in $${\rm O}\left( {\sqrt N } \right)$$ time as its design is based on Grover’s quantum search algorithm. To our knowledge, quantum algorithms for single pattern matching are available with limitations, and no algorithm has designed for multiple pattern matching. The main objective is to design quantum algorithm for both single and multiple patterns on a processing architecture of quantum random access memory $$\left( {QuRAM} \right)$$ . This gives a significant advantage to process large text databases in an efficient manner. Our complexity analysis justifies that the quantum algorithmic solutions achieve computational speedup over classical methods. We summarize the emergence of quantum-based pattern matching algorithms to process biological applications. The simulation is additionally done to validate and analyze the performance of proposed quantum algorithms. Lastly, we justify that our algorithms outperform the classical and quantum solutions and they are competent for implementing over quantum computer.

Finding patterns and periods in Cartesian tree matching

We introduce a new metric of match, called Cartesian tree matching, which means that two strings match if they have the same Cartesian trees. Based on Cartesian tree matching, we define single pattern matching, multiple pattern matching, and indexing problems. We propose a linear time algorithm for single pattern matching, and randomized linear time algorithms for multiple pattern matching and indexing. We also define three types of periods, called full period, initial period, and general period. We propose O(n) time, O(nlog⁡log⁡n) time, and O(nlog⁡n) time algorithms for finding all the full periods, initial periods, and general periods of a string of length n, respectively. Our efficient algorithms use a representation of the Cartesian tree, called parent-distance representation.

The Localization Method for Ship Network Nodes Based on High Performance Matching of Multi-pattern String TMWM

ABSTRACT Zhu, H. and Zhu, L., 2018. The localization method for ship network nodes based on high performance matching of multi-pattern string TMWM. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 776–780. Coconut Creek (Florida), ISSN 0749-0208. The pattern string matching used in the current method is single pattern matching. The matching efficiency and accuracy are low, and a large number of CPU resources are used, which reduces the efficiency and accuracy of the localization of the ship network nodes. In this paper, a high performance matching method based on multi-pattern string TMWM is proposed to preprocess the ship network nodes and make multi-pattern matching, until all the strings are terminated. Application of multi-pattern matching algorithm improves the matching efficiency, and will not take up too much CPU resources, which can increase the efficiency and accuracy of localization. The use of mobile beac...

New algorithm for multiple pattern matching using least count of pattern

Rapid advances in genome research in the past have resulted in generation of large set of data for DNA and protein sequences from different prokaryotic and eukaryotic genomes. DNA is made up of four nitrogenous bases known as adenine (A), guanine (G), thymine (T) and cytosine (C). Pattern matching technique generally divides into two categories i.e. single pattern matching and multiple pattern matching. When it is required to find all occurrences of the pattern in the given string, it is known as single pattern matching. When more than one pattern are matched against the given string simultaneously, it is known as multiple pattern matching. The study of pattern matching is one of the applications in the field of bioinformatics. Many algorithms are available in literature for pattern matching. The purpose of pattern matching algorithm is to reduce the number of character comparisons. Hence, in this paper an algorithm Multiple Pattern Matching using Least Count of Pattern (MPMLCP) has been proposed for multiple pattern matching. The proposed algorithm has been compared with the available algorithm and it has been shown that the number of comparisons reduces over the available algorithms. SAS 9.3 software package has been used for calculating the number of comparisons.

Research of an Improved Double-Array Trie Tree Algorithm

This paper presents an improved algorithm-iDAT, which is based on Double-Array Trie Tree for Chinese Word Segmentation Dictionary. Chinese word segmentation dictionary based on the Double-Array Trie Tree has higher efficiency of search, but the dynamic insertion will consume a lot of time. After initialization the original dictionary. We implement a Hash process to the empty sequence index values for base array. The final Hash table stores the sum of the empty sequence before the current empty sequence. This algorithm adopt Sunday jumps algorithm of Single Pattern Matching. With slightly and reasonable space cost increasing, iDAT reduces the average time complexity of the dynamic insertion process in Trie Tree. Practical results shows it has a good operation performance.

Novel Devaki Paul Algorithm for Multiple Pattern Matching

matching is one of the major issues in the area of network security and also in many other areas. The increase in network speed and traffic may cause the existing algorithms to become a performance bottleneck. Therefore, it is very necessary to develop more efficient pattern matching algorithm, in order to overcome troubles on performance. There are several algorithms in use, in which, DP algorithm (Devaki - Paul algorithm) is yielding good results in many cases. However, this algorithm was proposed only for the single pattern matching. But, now-a-days it is very necessary to find the multiple patterns in NIDS etc. In this paper, we are giving a new proposal to the DP algorithm (Devaki - Paul algorithm) to make it efficient and works also for the multiple pattern matching. The algorithm was tested and validated and the results have proved that the performance of DP algorithm is better than BM algorithm (Boyer - Moore algorithm) and the Quick Search algorithm. In case of tests with repeated character, its performance is greater than 1%~50% with BM and Quick Search algorithms. In case of tests with the English Text and Random Pattern, it's greater than 33%~91% with BM and 37%~85% with Quick Search algorithms. In case of tests with the English Text and Random Pattern of an unsuccessful search, its performance is greater than BM and Quick Search algorithms with 100%, if either the first and/or the last character of the pattern in the given text were not present.

The factors analysis and algorithm implementation of single-pattern matching

By studying the algorithms of single pattern matching, five factors that have effect on time complexity of the algorithm are analyzed. The five factors are: sorting the characters of pattern string in an increasing order of using frequency, utilizing already-matched pattern suffix information, utilizing already-matched pattern prefix information, utilizing the position factor which is absorbed from quick search algorithm, and utilizing the continue-skip idea which is originally proposed by this paper. Combining all the five factors, a new single pattern matching algorithm is implemented. It’s proven by the experiment that the efficiency of new algorithm is the best of all algorithms.