Approximate Pattern Matching Problem Research Articles

Absent words in a sliding window with applications

An absent word of a word y is a word that does not occur in y. It is then called minimal if all its proper factors occur in y. In fact, minimal absent words (MAWs) provide useful information about y and thus have several applications. In this paper, we propose an algorithm that maintains the set of MAWs of a fixed-length window sliding over y online. Our algorithm represents MAWs through nodes of the suffix tree. Specifically, the suffix tree of the sliding window is maintained using modified Senft's algorithm (Senft, 2005), itself generalizing Ukkonen's online algorithm (Ukkonen, 1995). We then apply this algorithm to the approximate pattern-matching problem under the Length Weighted Index distance (Chairungsee and Crochemore, 2012). This results in an online O(σ|y|)-time algorithm for finding approximate occurrences of a word x in y, |x|≤|y|, where σ is the alphabet size.

BAŞLICA BİYOİNFORMATİK ALGORİTMALARI İÇİN WEB ARA YÜZÜ VE YENİ OTOMAT TABANLI YAKLAŞIK DESEN EŞLEŞTİRME YAKLAŞIMI

In this study, we present a new web interface for major bioinformatics algorithms and introduce a novel approximate string matching algorithm. Our web interface executes major algorithms on the field for the use of computational biologists, students or any other interested researchers. In the web interface, algorithms come under three sections: Sequence alignment, pattern matching and motif finding. In each section, we introduce algorithms in order to find best fitting one for specific dataset and problem. The interface introduces execution time, memory usage and context specific results of algorithms such as alignment score. The interface utilizes emerging open source languages and tools. In order to develop light and user-friendly interface, all parts of the interface coded with Python language. On the other hand, Django is used for web interface. Second contribution of the study is novel A-BOM algorithm, which is designed for approximate pattern matching problem. The algorithm is approximate matching variation of Backward Oracle Matching. We compare our algorithm with popular approximate string matching algorithms. Results denote that A-BOM introduces %30 to %80 short runtime improvement when compared to current approximate pattern matching algorithms on long patterns.

A Practical Semi-External Memory Method for Approximate Pattern Matching

The approximate pattern matching problem (APM) consists in locating all occurrences of a given pattern P in a text T allowing a specific amount of errors. Due to the character of real applications and the fact that solutions do not have error-free nature in Computer Science, solving APM is crucial for developing meaningful applications. Recently, Ilie, Navarro and Tinta presented a fast algorithm to solve APM based on the well-known Landau-Vishkin algorithm. However, the amount of available memory limits the usage of their algorithm, since it requires all the answer array be in memory. In this article, a practical semi-external memory method to solve APM is presented. The method is based on the direct-comparison variation from Ilie et al.'s algorithm. Performance tests with real data of length up to 1.2 GB showed that the presented method is about 5 times more space-efficient than Ilie et al.'s algorithm and yet, has a competitive trade-off regarding time.

Approximate pattern matching with gap constraints

Pattern matching is a key issue in sequential pattern mining. Many researchers now focus on pattern matching with gap constraints. However, most of these studies involve exact pattern matching problems, a special case of approximate pattern matching and a more challenging task. In this study, we introduce an approximate pattern matching problem with Hamming distance. Its objective is to compute the number of approximate occurrences of pattern P with gap constraints in sequence S under similarity constraint d. We propose an efficient algorithm named Single-rOot Nettree for approximate pattern matchinG with gap constraints (SONG) based on a new non-linear data structure Single-root Nettree to effectively solve the problem. Theoretical analysis and experiments demonstrate an interesting law that the ratio M( P, S, d)/ N( P, S, m) approximately follows a binomial distribution, where M( P, S, d) and N( P, S, m) are the numbers of the approximate occurrences whose distances to pattern P are d (0≤ d≤ m) and no more than m (the length of pattern P), respectively. Experimental results for real biological data validate the efficiency and effectiveness of SONG.

Error Tree: A Tree Structure for Hamming and Edit Distances and Wildcards Matching

Approximate pattern matching is a fundamental problem in the bioinformatics and information retrieval applications. The problem involves different matching relations such as Hamming distance, edit distances, and the wildcards matching problem. The input is usually a text of length n over a fixed alphabet of length Σ, a pattern of length m, and an integer k. The output is to find all positions that have ≤ k Hamming distance, edit distance, or wildcards matching with P. Many algorithms and indexes have been proposed to solve the problems more efficiently, but due to the space and time complexities of the problems, most tools adopted heuristics approaches based on, for instance, suffix tree, suffix array, or Burrows Wheeler Transform to reach practical implementations. Error Tree is a novel tree structure that is mainly oriented to solve the approximate pattern matching problems, using less space and faster computation time. The algorithm proposes for Hamming distance and wildcards matching a tree structure that needs [Formula: see text] words and takes [Formula: see text] in the average case) of query time for any online/offline pattern, where occ is the number of outputs. In addition, a tree structure of [Formula: see text] words and [Formula: see text] in the average case) query time for edit distance for any online/offline pattern.

Indexed Swap Matching for Short Patterns

Let T be a text of length n and P be a pattern of length m, both strings over a fixed finite alphabet. The Pattern Matching with Swaps problem is to find all occurrences of P in T if adjacent pattern characters can be swapped. In the Approximate Pattern Matching problem with Swaps, one seeks for every text location with a swapped match of P, the number of swaps necessary to obtain a match at the location. In this paper we provide the first off-line solution for the swap matching problem and the approximate pattern matching problem with swaps. We present a new data-structure called a Swap-transforming Tree. And we give a precise upper-bond of the number of the swapped versions of a pattern. By using the swap-transforming tree, we can solve both problems in time O(λmlog 2n) on an O(nHk) bits indexing data structure. Here λ is a constant. Our solution is more effective when the pattern is short.

Approximate string matching with stuck address bits

A string S ∈ Σ m can be viewed as a set of pairs { ( s i , i ) ∣ s i ∈ S , i ∈ { 0 , … , m − 1 } } . We follow the recent work on pattern matching with address errors and consider approximate pattern matching problems arising from the setting where errors are introduced to the location component ( i ), rather than the more traditional setting, where errors are introduced to the content itself ( s i ). Specifically, we continue the work on string matching in the presence of address bit errors. In this paper, we consider the case where bits of i may be stuck, either in a consistent or transient manner. We formally define the corresponding approximate pattern matching problems, and provide efficient algorithms for their resolution.

Approximate string matching with address bit errors

A string S ∈ Σ m can be viewed as a set of pairs S = { ( σ i , i ) : i ∈ { 0 , … , m − 1 } } . We consider approximate pattern matching problems arising from the setting where errors are introduced to the location component ( i ), rather than the more traditional setting, where errors are introduced into the content itself ( σ i ). In this paper, we consider the case where bits of i may be erroneously flipped, either in a consistent or transient manner. We formally define the corresponding approximate pattern matching problems, and provide efficient algorithms for their resolution, while introducing some novel techniques.

Finding approximate patterns in undirected acyclic graphs

We consider an approximate pattern matching problem for undirected acyclic graphs. Specifically, let P be a pattern graph, D a data graph and t an integer. We present an algorithm to locate a subgraph in D whose distance from P is at most t. The distance measure used here is the degree-2 metric published previously. The time complexity of our algorithm is O(N PN Dd d log d) where N P and N D are the number of nodes in P and D, respectively; d=min{ d P , d D }; d P and d D are the maximum degree of P and D, respectively. Central to our algorithm is a procedure for finding approximate patterns in rooted unordered trees and freely allowing cuts. We discuss two applications of the algorithms in chemical information search and website management on the Internet.

Pattern Matching in Hypertext

The importance of hypertext has been steadily growing over the past decade. The Internet and other information systems use hypertext format, with data organized associatively rather than sequentially or relationally. A myriad of textual problems have been considered in the pattern matching field with many nontrivial results. Nevertheless, surprisingly little work has been done on the natural combination of pattern matching and hypertext. In contrast to regular text, hypertext has a nonlinear structure and the techniques of pattern matching for text cannot be directly applied to hypertext. Manber and Wu (1992, “IAPR Workshop on Structural and Syntactic Pattern Recognition, Bern, Switzerland”) pioneered the study of pattern matching in hypertext and defined a hypertext model for pattern matching. Akutsu (1993, “Procedures of the 4th Symposium on Combinatorial Pattern Matching, Podova, Italy,” pp. 1–10) developed an algorithm that can be used for exact pattern matching in a tree-structured hypertext. Park and Kim (1995, “6th Symposium on Combinatorial Pattern Matching, Helsinki, Finland”) considered regular pattern matching in hypertext. They developed a complex algorithm that works for hypertext with an underlying structure of a DAG. In this paper we present a much simpler algorithm achieving the same complexity which runs on any hypertext graph. We then extend the problem to approximate pattern matching in hypertext, first considering hamming distance and then edit distance. We show that in contrast to regular text, it does make a difference whether the errors occur in the hypertext or the pattern. The approximate pattern matching problem in hypertext with errors in the hypertext turns out to be NP-complete and the approximate pattern matching problem in hypertext with errors in the pattern has a polynomial time solution.

Stonn: A neural network IC using stochastic techniques: [formula omitted], and [formula omitted]. c/o Jim Symon, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-3175 USA

The approximate pattern matching problem (APM) consists in locating all occurrences of a given pattern P in a text T allowing a specific amount of errors. Due to the character of real applications and the fact that solutions do not have error-free nature in Computer Science, solving APM is crucial for developing meaningful applications. Recently, Ilie, Navarro and Tinta presented a fast algorithm to solve APM based on the well-known Landau-Vishkin algorithm. However, the amount of available memory limits the usage of their algorithm, since it requires all the answer array be in memory. In this article, a practical semi-external memory method to solve APM is presented. The method is based on the direct-comparison variation from Ilie et al.'s algorithm. Performance tests with real data of length up to 1.2 GB showed that the presented method is about 5 times more space-efficient than Ilie et al.'s algorithm and yet, has a competitive trade-off regarding time.