Number Of Edit Operations Research Articles

On the complexity of approximately matching a string to a directed graph

The problem of matching a query string to a directed graph, whose vertices are labeled by strings, has application in different fields. In this paper we present results on the complexity of the approximate matching problem, where edit operations are symbol substitutions and are allowed only to the graph labels or both to the graph labels and the query string. We show that deciding if there exists a path in a graph that represents a query string with edit operations to vertex labels is an NP-complete problem and it is fixed-parameter tractable when parameterized by the length of the input string. We show that the variants of approximate string matching we consider are not fixed-parameter tractable, when the parameter is (1) the length of the query string and (2) the number of edit operations. Moreover, we provide inapproximability results for these variants of the of approximate string matching problem.

Edit distance computation with minimum number of edit operations in database management system and information retrieval

Edit distance computation with minimum number of edit operations in database management system and information retrieval

Levenshtein Distances Fail to Identify Language Relationships Accurately

The Levenshtein distance is a simple distance metric derived from the number of edit operations needed to transform one string into another. This metric has received recent attention as a means of automatically classifying languages into genealogical subgroups. In this article I test the performance of the Levenshtein distance for classifying languages by subsampling three language subsets from a large database of Austronesian languages. Comparing the classification proposed by the Levenshtein distance to that of the comparative method shows that the Levenshtein classification is correct only 40% of time. Standardizing the orthography increases the performance, but only to a maximum of 65% accuracy within language subgroups. The accuracy of the Levenshtein classification decreases rapidly with phylogenetic distance, failing to discriminate homology and chance similarity across distantly related languages.This poor performance suggests the need for more linguistically nuanced methods for automated language classification tasks.

A bit-vector algorithm for computing Levenshtein and Damerau edit distances

The edit distance between strings A and B is defined as the minimum number of edit operations needed in converting A into B or vice versa. The Levenshtein edit distance allows three types of operat...

Fast similarity search in three-dimensional structure databases

Given a database D of three-dimensional (3D) molecular structures and a target molecule Q, the similarity search problem is to find the molecules O in D that match Q after allowing for an arbitrary number of whole-structure rotations and translations as well as a certain number of edit operations. The edit operations include relabeling an atom, deleting an atom, and inserting an atom. This search operation arises in many biochemical applications. In this paper we study the similarity search problem and a class of related queries. We present a computer vision based technique, called geometric hashing, for processing these queries. Experimental results on a database of 3D molecular structures obtained from the National Cancer Institute indicate the good performance of the presented technique.

A probabilistic algorithm for updating files over a communication link

Let P and Q be two parties on either side of a communication link. Assume that P has an old version of some file and wants to get a newer version from Q (who has it). Our algorithm performs this operation using poly( log|x|, log|y|,d(x,y), log(1/ε)) communication bits where x and y are old and new versions, d(x,y) is the number of editing operations needed to transform x into y, and ε is the error probability. The running time is poly(|x|,|y|, log(1/ε)) .

Constrained many-to-one string editing with memory

A new constrained edit distance between an ordered set of input strings and a single output string is proposed, where all the strings are over a finite alphabet. Editing transformation includes deletion of symbols from the input strings, combination of the decimated input strings by a function with memory, and substitution and insertion of symbols in the combination string. Constraints are related to the total number of edit operations and to the maximum numbers of consecutive deletions and insertions. A recursive algorithm for efficient computation of the constrained edit distance is derived and its computational complexity is determined. Potential applications in cryptanalysis of shift-register-based stream ciphers and in decoding for parallel communication channels with synchronization errors are also discussed.

Order- n correction for regular languages

A method is presented for calculating a string B , belonging to a given regular language L , which is “nearest” (in number of edit operations) to a given input string α . B is viewed as a reasonable “correction” for the possibly erroneous string α , where α was originally intended to be a string of L . The calculation of B by the method presented requires time proportional to | α |, the number of characters in α . The method should find applications in information retrieval, artificial intelligence, and spelling correction systems.