Combinatorial Pattern Matching 

About: Combinatorial Pattern Matching is an academic conference. The conference publishes majorly in the area(s): String (computer science) & Time complexity. Over the lifetime, 977 publications have been published by the conference receiving 22247 citations.

Approximate string-matching with q -grams and maximal matches

Abstract: We study approximate string matching in connection with two string distance functions that are computable in linear time. The first function is based on the so-called $q$-grams. An algorithm is given for the associated string matching problem that finds the locally best approximate occurences of pattern $P$, $|P|=m$, in text $T$, $|T|=n$, in time $O(n\log (m-q))$. The occurences with distance $\leq k$ can be found in time $O(n\log k)$. The other distance function is based on finding maximal common substrings and allows a form of approximate string matching in time $O(n)$. Both distances give a lower bound for the edit distance (in the unit cost model), which leads to fast hybrid algorithms for the edit distance based string matching.

Linear-Time Longest-Common-Prefix Computation in Suffix Arrays and Its Applications

Abstract: We present a linear-time algorithm to compute the longest common prefix information in suffix arrays As two applications of our algorithm, we show that our algorithm is crucial to the effective use of block-sorting compression, and we present a linear-time algorithm to simulate the bottom-up traversal of a suffix tree with a suffix array combined with the longest common prefix information

Identifying and Filtering Near-Duplicate Documents

Abstract: The mathematical concept of document resemblance captures well the informal notion of syntactic similarity. The resemblance can be estimated using a fixed size "sketch" for each document. For a large collection of documents (say hundreds of millions) the size of this sketch is of the order of a few hundred bytes per document. However, for effcient large scale web indexing it is not necessary to determine the actual resemblance value: it suffces to determine whether newly encountered documents are duplicates or near-duplicates of documents already indexed. In other words, it suffces to determine whether the resemblance is above a certain threshold. In this talk we show how this determination can be made using a "sample" of less than 50 bytes per document. The basic approach for computing resemblance has two aspects: first, resemblance is expressed as a set (of strings) intersection problem, and second, the relative size of intersections is evaluated by a process of random sampling that can be done independently for each document. The process of estimating the relative size of intersection of sets and the threshold test discussed above can be applied to arbitrary sets, and thus might be of independent interest. The algorithm for filtering near-duplicate documents discussed here has been successfully implemented and has been used for the last three years in the context of the AltaVista search engine.

Space efficient linear time construction of suffix arrays

Abstract: We present a linear time algorithm to sort all the suffixes of a string over a large alphabet of integers. The sorted order of suffixes of a string is also called suffix array, a data structure introduced by Manber and Myers that has numerous applications in pattern matching, string processing, and computational biology. Though the suffix tree of a string can be constructed in linear time and the sorted order of suffixes derived from it, a direct algorithm for suffix sorting is of great interest due to the space requirements of suffix trees. Our result improves upon the best known direct algorithm for suffix sorting, which takes O(n log n) time. We also show how to construct suffix trees in linear time from our suffix sorting result. Apart from being simple and applicable for alphabets not necessarily of fixed size, this method of constructing suffix trees is more space efficient.

Proximity Matching Using Fixed-Queries Trees

Abstract: We present a new data structure, called the fixed-queries tree, for the problem of finding all elements of a fixed set that are close, under some distance function, to a query element. Fixed-queries trees can be used for any distance function, not necessarily even a metric, as long as it satisfies the triangle inequality. We give an analysis of several performance parameters of fixed-queries trees and experimental results that support the analysis. Fixed-queries trees are particularly efficient for applications in which comparing two elements is expensive.