Number Of Character Comparisons Research Articles

Q‐frame hash comparison based exact string matching algorithms for DNA sequences

AbstractThe importance of string matching is due to its applications in many fields, such as medicine and bioinformatics. Various string matching algorithms are developed to speed up the search. Especially, hash‐based exact string matching algorithms are among the most time‐efficient ones. The efficiency of hash‐based approaches depends on the hash function. Hence, perfect hashing plays an essential role in hash‐based string matching. In this study, two q‐frame hash comparison‐based exact string matching algorithms, Hq‐QF and HqBM‐QF, are proposed. We have used a collision‐free perfect hash function for DNA sequences in the proposed algorithms. In the first approach, after hash values match for the last qcharacters, the character comparisons in the Hash‐q algorithm are replaced with q‐frame hash comparison. In the second approach, we improved the first approach by utilizing the shift size indicated at the th entry in the good suffix shift table. Since the number of character comparisons is minimized, the worst‐case time complexity of the proposed algorithms is . In both approaches, q‐frame hash comparisons replace most character comparisons as a trade‐off. The results show that the proposed approaches are more efficient than the Hash‐q algorithm in terms of runtime efficiency and the number of character comparisons.

The Enhanced Hybrid Algorithm for the AbdulRazzaq and Berry-Ravindran Algorithms

Exact string matching is one of the critical issues in the field of computer science. This study proposed a hybrid string matching algorithm called E- AbdulRazzaq. This algorithm used the best properties of two original algorithms; AbdulRazzaq and Berry-Ravindran Algorithms. The proposed algorithm showed an efficient performance in the number of attempts and number of character comparison when compared the original and recent to the standard algorithms. The proposed algorithm was applied in several types of databases, which are DNA sequences, Protein sequences, XML structures, Pitch characters, English texts, and Source codes. The Pitch database was the best match for E-AbdulRazzaq with the number of attempts involving long and short patterns, while the DNA database was the worst match. No data is specified as the best or worst with the E-AbdulRazzaq algorithm in terms of the character comparisons. The E-AbdulRazzaq algorithms ranked first in most databases when using short and long patterns, in terms of number of attempts and character comparisons.

Fast Hybrid String Matching Algorithm based on the Quick-Skip and Tuned Boyer-Moore Algorithms

The string matching problem is considered as one of the most interesting research areas in the computer science field because it can be applied in many essential different applications such as intrusion detection, search analysis, editors, internet search engines, information retrieval and computational biology. During the matching process two main factors are used to evaluate the performance of the string matching algorithm which are the total number of character comparisons and the total number of attempts. This study aims to produce an efficient hybrid exact string matching algorithm called Sinan Sameer Tuned Boyer Moore-Quick Skip Search (SSTBMQS) algorithm by blending the best features that were extracted from the two selected original algorithms which are Tuned Boyer-Moore and Quick-Skip Search. The SSTBMQS hybrid algorithm was tested on different benchmark datasets with different size and different pattern lengths. The sequential version of the proposed hybrid algorithm produces better results when compared with its original algorithms (TBM and Quick-Skip Search) and when compared with Maximum-Shift hybrid algorithm which is considered as one of the most recent hybrid algorithm. The proposed hybrid algorithm has less number of attempts and less number of character comparisons.

New Searching Technique of Hybrid Exact String Matching Algorithm

String matching algorithms play a significant role in solving the fundamental problems of computer sciences. This study proposed a new hybrid string matching algorithm called AbdulRazzaq. This algorithm consisted of a combination between the new searching technique and selecting the good features from two original algorithms, namely, Smith and Karp–Rabin. The proposed algorithm showed lower number of attempts and character comparisons than those of the original algorithms and common algorithms in the string matching field. AbdulRazzaq algorithm used six types of databases, namely, DNA, Protein, XML, Pitch, English, and Source. The best database regarding the number of attempts of AbdulRazzaq algorithm was Pitch, whereas DNA was the worst. This algorithm can work with good performance in the number of character comparisons with all databases.

Multikey Quickselect

We present multikey quickselect: an efficient, in-place, easy-to-implement algorithm for the selection problem for strings. We analyze its expected cost under a uniform model, measured as the number of character comparisons and pointer swaps, depending on the alphabet cardinality. From the analysis, we derive a binary variant of the algorithm, which is more efficient when the range of values for the alphabet is known. This variant can also be applied to multikey quicksort.

An Improved BM Pattern Matching Algorithm in Intrusion Detection System

An improved BM-algorithm in intrusion detection system was presented which can increase displacements using the next character of the substring has been matched in the main string and pattern string. Experimental results obtained by capturing network packets with the number of matches, the number of pattern moves, the number of character comparisons as the main indicators. Experimental results show that the number of matches after and before improved is equal, and the number of pattern moves decreased by about13.3% after improved, and the number of character comparisons decreased by about 15.1% after improved. This indicates that the improved algorithm improved the efficiency of pattern matching.

A Fast Hybrid Algorithm Approach for the Exact String Matching Problem Via Berry Ravindran and Alpha Skip Search Algorithms

Problem statement: String matching algorithm had been an essential means for searching biological sequence database. With the constant expansion in scientific data such as DNA and Protein; the development of enhanced algorithms have even become more critical as the major concern had always been how to raise the performances of these search algorithms to meet challenges of scientific information. Approach: Therefore a new hybrid algorithm comprising Berry Ravindran (BR) and Alpha Skip Search (ASS) is presented. The concept is based on BR shift function and combines with ASS to ensure improved performance. Results: The results obtained in percentages from the proposed hybrid algorithm displayed superior results in terms of number of attempts and number of character comparisons than the original algorithms when various types of data namely DNA, Protein and English text are applied to appraise the hybrid performances. The enhancement of the proposed hybrid algorithm performs better at 71%, 60% and 63% when compared to Berry-Ravindran in DNA, Protein and English text correspondingly. Moreover the rate of enhancement over Alpha Skip Search algorithm in DNA, Protein and English text are 48%, 28% and 36% respectively. Conclusion: The new proposed hybrid algorithm is relevant for searching biological science sequence database and also other string search systems.

A Fast Hybrid Algorithm for the Exact String Matching Problem

Problem statement: Due to huge amount and complicated nature of data being generated recently, the usage of one algorithm for string searching was not sufficient to ensure faster search and matching of patterns. So there is the urgent need to integrate two or more algorithms to form a hybrid algorithm (called BRSS) to ensure speedy results. Approach: This study proposes the combination of two algorithms namely Berry-Ravindran and Skip Search Algorithms to form a hybrid algorithm in order to boost search performance. Results: The proposed hybrid algorithm contributes to better results by reducing the number of attempts, number of character comparisons and searching time. The performance of the hybrid was tested using different types of data-DNA, Protein and English text. The percentage of the improvements of the hybrid algorithm compared to Berry-Ravindran in DNA, Protein and English text are 50%, 43% and 44% respectively. The percentage of the improvements over Skip Search algorithm in DNA, Protein and English text are 20%, 30% and 18% respectively. The criteria applied for evaluation are number of attempts, number of character comparisons and searching time. Conclusion: The study shows how the integration of two algorithms gives better results than the original algorithms even the same data size and pattern lengths are applied as test evaluation on each of the algorithms.

A NOVEL ALGORITHM FOR SOLVING THE STRING MATCHING PROBLEM

The string matching problem consists of finding one or more, generally all, exact occurrences of a pattern P in a text T. This paper presents a new algorithm for solving the string matching problem. Application of the proposed algorithm assists in improving the search process of a specific pattern in a certain unchangeable text through decreasing the number of character comparisons. Operation concept of such an algorithm depends on pattern reading to obtain the pattern length and the pattern first character and then a search is done in a table of two columns: the first column represents the word length in the text and the second one represents the start positions of each word classified by the same length. After that the algorithm just searches the words of the same length. Our experimental results depend mainly on comparing the performance of our algorithm with the well-known pattern matching algorithms such as Boyer–Moor's and Boyer–Moor–Galil's. The comparison between our algorithm and others are done in terms of the number of characters compared for different sizes of text. The output results show that our algorithm performs better than the others in terms of this parameter.

Fast string matching for multiple searches

We present a string matching or pattern matching method which is especially useful when a single block of text must be searched repeatedly for different patterns. The method combines linking the text according to digrams, searching on the least-frequent digram, and probing selected characters as a preliminary filter before full pattern comparison. Tests on real alphabetic data show that the number of character comparisons may be decreased by two orders of magnitude compared with Knuth–Morris–Pratt and similar searching, but with an initialization overhead comparable to five to ten conventional searches. Copyright © 2001 John Wiley & Sons, Ltd.

On-line string matching algorithms: survey and experimental results

In this paper we present a short survey and experimental results for well known sequential string matching algorithms. We consider algorithms based on different approaches including classical, suffix automata, bit-parallelism and hashing. We put special emphasis on algorithms recently presented such as Shift-Or and BNDM algorithms. We compare these algorithms in terms of the number of character comparisons and the running time for four different types of text: binary alphabet, alphabet of size 8, English alphabet and DNA alphabet.

On the Exact Complexity of String Matching: Lower Bounds

It is shown that, for any pattern of length m and for any text of length n, it is possible to find all occurrences of the pattern in the text in overall linear time and at most $\frac{4}{3}n - \frac{1}{3}m$ character comparisons. In fact, the bound on the number of character comparisons is usually tighter than this, for the bound is expressed in terms of the structure of the pattern. The algorithm here need not have any knowledge of the alphabet. This improves the best previous bound of $1.5n-.5(m - 1)$ obtained by Colussi [Inform. and Comput., to appear] and Apostolico and Crochemore [Tech. Report TR89-75, LITP, Universite de Paris, Paris, France, 1989]. In a companion paper [SIAM J. Comput., 20 (1991), pp. 1008–1020], the authors show a lower bound for on-line algorithms that is equal to $\frac{4}{3}n - \frac{1}{3}m$ for $m = 3$. For $m = 1,2,n$ character comparisons is optimal. This algorithm is based on a new analysis of the string matching algorithm by Colussi. Moreover, this new analysis of Colussi’...

Structural properties of the string statistics problem

A suitably weighted Index Tree such as a B-tree or a Suffix Tree can be easily adapted to store, for a given string x and for all substrings w of x, the number of distinct instances of w along x. The storage needed is seen to be linear in the length of x: moreover, the whole statistics can itself be derived in linear time, off-line of a RAM. If the substring w has nontrivial periods, however, the number of distinct instances might differ from that of distinct non-overlapping occurrences along x. It is shown here that O( n log n) storage units— n standing for the length of x—are sufficient to organize this second kind of statistics, in such a way that the maximum number of nonoverlapping instances for arbitrary w along x can be retrieved in a number of character comparisons not exceeding the length of w.