S.M. Vidanagamachchi, S.D. Dewasurendra, R.G. Ragel, M.Niranjan · Bulletin of Miscellaneous Information (Royal Gardens Kew)
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs.
Cited by 0
Sugandima Vidanagamachchi, S. D. Dewasurendra, Roshan Ragel et al. · International Journal of Research in Computer Science · 2012
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs..
Cited by 11
Mrs F. Van Neerven, B. W. Watson, G. Zwaan · 1992
This paper presents a taxonomy of keyword pattern matching algorithms, including the wellknown Knuth-Morris-Pratt, Aho-Corasick, Boyer-Moore, and Commentz-Walter algorithms and a number of their variants. The taxonomy is based on the idea of ordering algorithms according to their essential problem and algorithm details, and deriving all algorithms from a common starting point by adding these details in a correctness preserving way. This way of presentation not only provides a complete correctness argument of each algorithm, but also makes very clear what algorithms have in common (the details of their nearest common ancestor) and where they differ (the details added after their nearest common ancestor). Moreover, the paper provides complete derivations of the intricate precomputation algorithms, some of which either can not be found in the literature (Commentz-Walter) or are given in several different versions (Boyer-Moore). Contents 1 Introduction and related work 1 I The algorithm...
Cited by 0
BW Bruce Watson · TU/e Research Portal (Eindhoven University of Technology) · 1994
This paper presents a toolkit of pattern matching algorithms, performance data on the algorithms, and recommendations for the selection of an algorithm (given a particular application) . The pattern matching problem is: given a finite non-empty set of keywords and an input string, find all occurrences of any of the keywords in the input string. The pattern matching toolkit (written in the C programming language, and freely available) contains implementations of the Knuth-Morris-Pratt, Boyer-Moore, Aho-Corasick, and Commentz-Walter algorithms. The algorithms are implemented directly from the abstract algorithms derived and presented in the taxonomy of Watson and Zwaan [WZ92]. The toolkit provides one of the few known correct implementations of the Commentz-Walter precomputation algorithm. The performance of all of the algorithms (running on a variety of workstation hardware) was measured on two types of input: English text and genetic sequences. The input data, which is the same as that...
Cited by 31Open access
Zeeshan Ahmed Khan, R. K. Pateriya, Maulana Azad · 2012
Abstract—String matching algorithms in software applications like virus scanners (anti-virus) or intrusion detection systems is stressed for improving data security over the internet. String-matching techniques are used for sequence analysis, gene finding, evolutionary biology studies and analysis of protein expression. Other fields such as Music Technology, Computational Linguistics, Artificial Intelligence, Artificial Vision, have been using string matching algorithm as their integral part of theoretical and practical tools. There are various problems in string matching appeared as a result of such continuous, exhaustive use, which in turn were promptly solved by the computer scientists. The more practical solutions to the real world problems can be solved by the multiple pattern string matching algorithms. String Matching Algorithms like Aho-Corasick, Commentz-Walter, Bit parallel, Rabin-Karp, Wu-Manber etc. are to be focused in this paper. Aho-Corasick algorithm is based on finite state machines (automata). Commentz Walter algorithm is based on the idea of Knutt-Morris-Pratt and finite state machines. Bit parallel algorithm like shift-or makes use of wide machine words (CPU registers) to parallelize the work. Rabin-Karp uses hashing to find any one of a set of pattern strings in a text. Wu-Manber looking text in blocks instead of one by one character combining idea of Aho-Corasick and Boyer-Moore. Each algorithm has certain advantages and disadvantages. This paper presents the comparative analysis of various multiple pattern string matching algorithms. A comparison of Aho-Corasick, Commentz-Walter, Bit-Parallel(Shift-OR), Rabin-Karp, Wu-Manber etc. type of string matching algorithms is presented on different parameters.
Cited by 16
Bruce W. Watson · TU/e Research Portal (Eindhoven University of Technology) · 1995
Abstract. In this paper, I outline a new algorithm for regular tree pattern matching. The Boyer-Moore family of string pattern matching algorithms are considered to be among the most ecient. The Boyer-Moore idea of a shift dis-tance was generalized by Commentz-Walter for multiple keywords, and general-izations for regular expressions have also been found. The existence of a further generalization to tree pattern matching was rst mentioned in the statements accompanying my dissertation, [Wats95]. Key words: tree pattern matching, tree parsing, code selection, Boyer-Moore
Cited by 5
Prof. Dr. M. Rem, B. W. Watson, G. Zwaan · 1995
This paper presents a taxonomy of sublinear keyword pattern matching algorithms related to the Boyer-Moore algorithm [BM77] and the Commentz-Walter algorithm [CW79a, CW79b]. The taxonomy includes, amongst others, the multiple keyword generalization of the single keyword Boyer-Moore algorithm and an algorithm by Fan and Su [FS93, FS94]. The corresponding precomputation algorithms are presented as well. The taxonomy is based on the idea of ordering algorithms according to their essential problem and algorithm details, and deriving all algorithms from a common starting point by successively adding these details in a correctness preserving way. This way of presentation not only provides a complete correctness argument of each algorithm, but also makes very clear what algorithms have in common (the details of their nearest common ancestor) and where they differ (the details added after their nearest common ancestor). Introduction of the notion of safe shift distances proves to be essential ...
Cited by 0
Watson Zwaan Faculty, Prof. Dr. M. Rem, B. W. Watson et al. · 1995
This paper presents a taxonomy of sublinear keyword pattern matching algorithms related to the Boyer-Moore algorithm [BM77] and the Commentz-Walter algorithm [CW79a, CW79b]. The taxonomy includes, amongst others, the multiple keyword generalization of the single keyword Boyer-Moore algorithm and an algorithm by Fan and Su [FS93, FS94]. The corresponding precomputation algorithms are presented as well. The taxonomy is based on the idea of ordering algorithms according to their essential problem and algorithm details, and deriving all algorithms from a common starting point by successively adding these details in a correctness preserving way. This way of presentation not only provides a complete correctness argument of each algorithm, but also makes very clear what algorithms have in common (the details of their nearest common ancestor) and where they differ (the details added after their nearest common ancestor). Introduction of the notion of safe shift distances proves to be essential ...
Cited by 0
Jonathan Cohen · Software Practice and Experience · 1998
A method of full-text scanning for matches in a large dictionary of keywords is described, suitable for Selective Dissemination of Information (SDI). The method is applicable to large dictionaries (say 104 to 105 entries), and to arbitrary byte streams for both patterns and data samples. The approach involves a sequence of tests, beginning with Boyer–Moore–Horspool skipping on digrams, followed by a succession of hash tests, and completed by trie searching, the combination of which is quite fast. Background information is provided, the algorithm and its implementation are described in detail, and experimental results are presented. In particular, tests suggest that the proposed method outperforms the algorithms of Aho–Corasick and Commentz–Walter when implementing large dictionaries. © 1998 John Wiley & Sons, Ltd.
Cited by 3
Bruce W. Watson, G. Zwaan · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 1995
This article presents a taxonomy of sublinear keyword pattern matching algorithms related to the Boyer-Moore algorithm [3] and the Commentz-Walter algorithm [5, 6]. The taxonomy includes, amongst others, the multiple keyword generalization of the single keyword Boyer-Moore algorithm and an algorithm by Fan and Su [9, 10]. The corresponding precomputation algorithms are presented as well. The taxonomy is based on the idea of ordering algorithms according to their essential problem and algorithm details, and deriving all algorithms from a common starting point by successively adding these details in a correctness preserving way. This way of presentation not only provides a complete correctness argument of each algorithm, but also makes very clear what algorithms have in common (the details of their nearest common ancestor) and where they differ (the details added after their nearest common ancestor). Introduction of the notion of safe shift distances proves to be essential in the derivation and classification of the algorithms. Moreover, the article provides a common derivation for and a uniform presentation of the precomputation algorithms, not yet found in the literature.
Cited by 0Open access
Jonathan Cohen · Software Practice and Experience · 1998
A method of full-text scanning for matches in a large dictionary of keywords is described, suitable for Selective Dissemination of Information (SDI). The method is applicable to large dictionaries (say 104 to 105 entries), and to arbitrary byte streams for both patterns and data samples. The approach involves a sequence of tests, beginning with Boyer–Moore–Horspool skipping on digrams, followed by a succession of hash tests, and completed by trie searching, the combination of which is quite fast. Background information is provided, the algorithm and its implementation are described in detail, and experimental results are presented. In particular, tests suggest that the proposed method outperforms the algorithms of Aho–Corasick and Commentz–Walter when implementing large dictionaries. © 1998 John Wiley & Sons, Ltd.
Cited by 4
2008
String matching problem has been received extensive research, most of which follow a common procedure, i.e., compare keywords with substring of text within a fixed length window, and then shift the window from left to right as far as possible. According to the way that patterns are compared with the text in the window, string matching algorithms can be categorized into three classes: prefix searching, suffix searching and factor searching. In prefix searching methods, window-shifting is accomplished through computing the longest common prefix between the text and the patterns. There are two ways to compute the length of the longest prefix. The first way is to compute the longest suffix of the text read that is also a prefix of the string, typified by the famous KMP� � Knuth-Morris-Pratt� � algorithm [2] and the Aho-Corasick algorithm[3]. The second maintain a set of prefix of keywords that are also suffix of text, and update the set at each character read. This is what the Shift-And and the Shift-Or algorithm do[4]. The suffix searching approach consists in searching backward for a suffix of the window that is also a suffix of the keyword, which features the backward searching from right to left within the window. The Boyer-Moore algorithm[5] is of this kind, and so are the Commentz-Walter algorithm [6]and the Wu-Manber algorithm[7].
Cited by 0