String Matching Methodologies:A Comparative Analysis

String matching is the problem of finding all occurrences of a character pattern in a text. This paper provides an overview of different string matching algorithms and comparative study of these algorithms. In this paper, we have evaluated several algorithms, such as Naive string matching algorithm, Brute Force algorithm, Rabin-Karp algorithm, Boyer-Moore algorithm, KnuthMorris-Pratt algorithm, Aho-Corasick Algorithm and Commentz Walter algorithm. We analysed the core ideas of these single pattern string matching algorithms and multi-pattern string matching algorithms.We compared the matching efficiencies of these algorithms by searching speed, pre-processing time, matching time and the key ideas used in these algorithms. It is observed that performance of string matching algorithm is based on selection of algorithms used and also on network bandwidth. KeywordString matching, Naive Search, Rabin Karp, Boyer-Moore, KMP, Exact String Matching, Approximate String Matching, Comparison of String Matching Algorithms. I.INTRODUCTION String matching is a technique to find out pattern from given text. Let ∑ be an alphabet. Elements of ∑ are called symbols or characters. For example, if ∑ = {a, b}, then abab is a string over ∑. The pattern is denoted by P [1....m]. The text is denoted by T [1...n]. If P occurs with shift s in T, then we call s a valid shift; otherwise, we call s an invalid shift. The string matching problem is the problem of finding all valid shifts with which a given pattern P occurs in a given text T [1]. Figure 1 shows this definition [2]. Figure 1: String Matching Example II.EXACT STRING MATCHING ALGORITHMS Exact string matching is used in search of any occurrence of a string A in string B. These algorithms are applied in biology, and especially in the segment concerning DNA chains [5]. Much of data processing in bioinformatics involves in one way or another recognising certain patterns within DNA, RNA or protein sequences. A. Single pattern string matching algorithms 1) Naive string matching algorithm: It is also known as Brute Force algorithm. It has no pre-processing phase, needs constant extra space. It always shifts the window by exactly one position to the right. It requires 2n expected text characters comparisons. It finds all valid shifts using a loop that checks the condition P[1....m]=T[s+1........s+m] for each of the n-m+1 possible values of s . Consider the following example. T=ANPANMAN P=MAN ANPANMAN A brute force method for string matching algorithm is shown in Figure 2: Figure 2: Naive String Matching Example Naive string matching algorithm takes time O((nm+1)m), and this bound is tight in the worst case. The worst case running time is thus O((n-m+1)m)[4]. The running time of Naive String Matching algorithm is equal to its matching time, since there is no preprocessing. 2) Rabin Karp String Matching Algorithm: This algorithm uses hashing function. It works in two phases i.e. pre-processing phase (time complexity Θ(m)),matching phase(time complexity average Θ (n+m),worst Θ((n-m+1) m)).[4] Rabin Karp matcher is used to find a numeric pattern P from a given text T. It firstly divides the pattern with a predefined prime number q to calculate the remainder of pattern P. Then it takes the first m characters from text T at first shift s to compute remainder of m characters from text T. If the remainder of the pattern P and remainder of the text T are equal, only then we compare the text with pattern otherwise there is no need for comparison. We will repeat the process for next set of characters from text for all possible shifts which are from s=0 to n-m. So, according to this, two numbers n1 Akhtar Rasool Amrita Tiwari et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 3 (2) , 2012,3394 3397