Proposed Model In String Matching Algorithm

proposed parallel string matching algorithm can reduce string matching time. Keywords string matching parallel string matching computing model omega model. I. I. ntroduction tring matching has been extensively studied in the past 30 years. A string C of length n is a sequence of characters C1C2Cn. Let Y1, Y2, YN

Fig.4.Number of algorithms proposed in the last 21 years 1990-2010 equal to the number of bits in a computer word. Thus, although string matching algorithms based on bit-parallelism are usually simple and have very low memory requirements, they generally work well with patterns of moderate length only.

The first optimal parallel string matching algorithm was proposed by Galil 5. On SIMD-CRCW model, this algorithm required n log n processors, and the time complexity is Olog n on SIMD-CREW model, it required n log2 n processors and the time complexity is Olog2 n. Vishkin 6 improved this algorithm to

Presented results show that it is possible to use the proposed methodology to build a domain model for selecting an optimal algorithm for the exact string matching. Except for optimal algorithm selection for a specific domain, this methodology can be used to improve the efficiency of string- matching algorithms in the context of performance

String matching can be done in one of two ways exact matching or approximate matching. The proposed research focuses on employing an exact string matching using Inclusive Supervised Learning Model to develop a Accurate String Matching ISL-ASM that is an upgraded form of the Boyer-Moore-Horspool algorithm. When compared to traditional models

The String Matching Algorithms Research Tool S. Faro y, T. Lecroqz, S. Borz , S. Di Mauro y, and A. Maggio yUniversit a di Catania, Viale A.Doria n.6, 95125 Catania, Italy zUniversit e de Rouen, LITIS EA 4108, 76821 Mont-Saint-Aignan Cedex, France Abstract. String matching is the problem of nding all occurrences of a given pattern in a given text.

This article presents a survey on single-pattern exact string matching algorithms.The main purpose of this survey is to propose new classification, identify new directions and highlight the

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

Traditional String Matching Algorithms Some foundational algorithms for string matching are summarized below KString Matching Beyond Exact Comparisons This section explores string matching, a technique used to find specific patterns within text. It has applications in search engines, biology, and data integration.

To assess the efficiency of the proposed models, the genome sequences of different sizes 10-100 Mb are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.