Approximate string matching

About: Approximate string matching is a research topic. Over the lifetime, 1903 publications have been published within this topic receiving 62352 citations. The topic is also known as: fuzzy string-searching algorithm & fuzzy string-matching algorithm.

Practical suffix tree construction

Abstract: Large string datasets are common in a number of emerging text and biological database applications. Common queries over such datasets include both exact and approximate string matches. These queries can be evaluated very efficiently by using a suffix tree index on the string dataset. Although suffix trees can be constructed quickly in memory for small input datasets, constructing persistent trees for large datasets has been challenging. In this paper, we explore suffix tree construction algorithms over a wide spectrum of data sources and sizes. First, we show that on modern processors, a cache-efficient algorithm with O(n2) complexity outperforms the popular O(n) Ukkonen algorithm, even for in-memory construction. For larger datasets, the disk I/O requirement quickly becomes the bottleneck in each algorithm's performance. To address this problem, we present a buffer management strategy for the O(n2) algorithm, creating a new disk-based construction algorithm that scales to sizes much larger than have been previously described in the literature. Our approach far outperforms the best known disk-based construction algorithms.

Multiseed Lossless Filtration

Abstract: We study a method of seed-based lossless filtration for approximate string matching and related bioinformatics applications. The method is based on a simultaneous use of several spaced seeds rather than a single seed as studied by Burkhardt and Karkkainen [1]. We present algorithms to compute several important parameters of seed families, study their combinatorial properties, and describe several techniques to construct efficient families. We also report a large-scale application of the proposed technique to the problem of oligonucleotide selection for an EST sequence database.

Indexing Text with Approximate q-Grams

Abstract: We present a new index for approximate string matching. The index collects text q-samples, i.e. disjoint text substrings of length q, at fixed intervals and stores their positions. At search time, part of the text is filtered out by noticing that any occurrence of the pattern must be reflected in the presence of some text q-samples that match approximately inside the pattern. We show experimentally that the parameterization mechanism of the related filtration scheme provides a compromise between the space requirement of the index and the error level for which the filtration is still effcient.

Random access to grammar-compressed strings

Abstract: Let S be a string of length N compressed into a context-free grammar S of size n We present two representations of S achieving O(log N) random access time, and either O(n · αk(n)) construction time and space on the pointer machine model, or O(n) construction time and space on the RAM Here, αk(n) is the inverse of the kth row of Ackermann's function Our representations also efficiently support decompression of any substring in S: we can decompress any substring of length m in the same complexity as a single random access query and additional O(m) time Combining these results with fast algorithms for uncompressed approximate string matching leads to several efficient algorithms for approximate string matching on grammar-compressed strings without decompression For instance, we can find all approximate occurrences of a pattern P with at most k errors in time O(n(min{|P|k, k4 +|P|} +log N) + occ), where occ is the number of occurrences of P in S Finally, we are able to generalize our results to navigation and other operations on grammar-compressed treesAll of the above bounds significantly improve the currently best known results To achieve these bounds, we introduce several new techniques and data structures of independent interest, including a predecessor data structure, two "biased" weighted ancestor data structures, and a compact representation of heavy-paths in grammars

Query by rhythm: an approach for song retrieval in music databases

Abstract: We propose techniques for retrieving songs by rhythm from music databases. The rhythm of songs is modeled by rhythm strings. The song retrieval problem is then transformed to the string matching problem. In order to allow approximate string matching, we define similarity measures on rhythm strings. An index structure, called L-tree, is proposed to support efficient sub-string matching. Retrieval algorithms based on L-tree are then designed to provide approximate and sub- song retrieval. Experimental results show that this approach is effective and efficient.