Scaled and permuted string matching

The goal of scaled permuted string matching is to find all occurrences of a pattern in a text, in all possible scales and permutations. Given a text of length n and a pattern of length m we present an O(n) algorithm.     

A filtering algorithm for k-mismatch with don't cares

We present a filtering based algorithm for the k-mismatch pattern matching problem with don't cares. Given a text t of length n and a pattern p of length m with don't care symbols in either p or t (but not both), and a bound k, our algorithm finds all the places that the pattern matches the text with at most k mismatches. The algorithm is deterministic and runs in Θ(nm1/3k1/3log2/3m) time.     

Fast string matching algorithms for run-length coded strings

Given a run-length coded text of length 2n and a run-length coded pattern of length 2m,m?n commonly, this paper first presents anO(n+m) time sequential algorithm for string matching, then presents anO(1) time parallel algorithm on a two-dimensionalm×n mesh with a reconfigurable bus system.     

Matching with don't-cares and a small number of mismatches

In matching with don't-cares and k mismatches we are given a pattern of length m and a text of length n, both of which may contain don't-cares (a symbol that matches all symbols), and the goal is to find all locations in the text that match the pattern with at most k mismatches, where k is a parameter. We present new algorithms that solve this problem using a combination of convolutions and a dynamic programming procedure. We give randomized and deterministic solutions that run in time O(nk²logm) and O(nk³logm), respectively, and are faster than the most efficient extant methods for small values of k. Our deterministic algorithm is the first to obtain an O(polylog(k)⋅nlogm) running time.     

A new method for approximate indexing and dictionary lookup with one error

We consider the problem of designing an efficient index for approximate pattern matching. Despite ongoing efforts, this topic is still a challenge in combinatorial pattern matching. We present a new data structure that allows to report all matches in worst-case time O(|Σ|m+occ), which is linear in the pattern length m and the number of occurrences occ for alphabets of constant size |Σ|. Our index uses O(n|Σ|logn) extra space on average and with high probability, where n is the length of the text (indexing) or the number of strings to index (dictionary lookup).     

String matching with inversions and translocations in linear average time (most of the time)

We present an efficient algorithm for finding all approximate occurrences of a given pattern p of length m in a text t of length n allowing for translocations of equal length adjacent factors and inversions of factors. The algorithm is based on an efficient filtering method and has an O(nmmax(α,β))-time complexity in the worst case and O(max(α,β,σ))-space complexity, where α and β are respectively the maximum length of the factors involved in any translocation and inversion, and σ is the alphabet size. Moreover we show that our algorithm has an O(n) average time complexity, whenever , for ε>0. Experiments show that the proposed algorithm achieves very good results in practical cases.     

Two-dimensional pattern matching by two-dimensional on-line tessellation acceptors

By reducing an array matching problem to a string matching problem in a natural way, it is shown that efficient string matching algorithms may be applied to arrays. In this paper, based on the ideas due to Baker, an application of the two-dimensional on-line tessellation acceptor (2-dota) is presented for very rapid on-line detection of occurrences of a fixed set of keyarrays as embedded subarrays in a text array. The main part of the algorithm described in this paper consists of constructing two finite state pattern (string) matching machines from the keyarrays. By combining these two finite state pattern matching machines, we construct the 2-dota which, given an m × n text array, solves the two-dimensional pattern matching problem in m + n?1 steps.     

分布式存储的并行串匹配算法的设计与分析

并行串匹配算法的研究大都集中在PRAM(parallel random access machine)模型上,其他更为实际的模型上的并行串匹配算法的研究相对要薄弱得多.该文采用将最优串行算法并行化的技术,利用模式串的周期性质,巧妙地将改进的KMP(Knuth-Morris-Pratt)算法并行化,提出了一个简便、高效且具有良好可扩放性的分布式串匹配算法,其计算复杂度为O(n/p+m),通信复杂度为O(ulogp相似文献   

Construction of Aho Corasick automaton in linear time for integer alphabets

We present a new simple algorithm that constructs an Aho Corasick automaton for a set of patterns, P, of total length n, in O(n) time and space for integer alphabets. Processing a text of size m over an alphabet Σ with the automaton costs O(mlog|Σ|+k), where there are k occurrences of patterns in the text.A new, efficient implementation of nodes in the Aho Corasick automaton is introduced, which works for suffix trees as well.     

Detecting False Matches in String-Matching Algorithms

Consider a text string of length n, a pattern string of length m, and a match vector of length n which declares each location in the text to be either a mismatch (the pattern does not occur beginning at that location in the text) or a potential match (the pattern may occur beginning at that location in the text). Some of the potential matches could be false, i.e., the pattern may not occur beginning at some location in the text declared to be a potential match. We investigate the complexity of two problems in this context, namely, checking if there is any false match, and identifying all the false matches in the match vector. We present an algorithm on the CRCW PRAM that checks if there exists a false match in O(1) time using O(n) processors. This algorithm does not require preprocessing the pattern. Therefore, checking for false matches is provably simpler than string matching since string matching takes time on the CRCW PRAM. We use this simple algorithm to convert the Karp—Rabin Monte Carlo type string-matching algorithm into a Las Vegas type algorithm without asymptotic loss in complexity. We also present an efficient algorithm for identifying all the false matches and, as a consequence, show that string-matching algorithms take time even given the flexibility to output a few false matches. Received January 28, 1995; revised January 17, 1996.