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About 4,921 papers for “string processing”

Combinatorial Methods for String Processing

Shunsuke Inenaga · 2021

Strings, or sequences, are the most fundamental form of digital data. Due to recent developments in sensor network technologies and semi-automated machine-to-machine (M2M) communications, sequential data have been increasing more rapidly than ever. It is often the case that such semi-automatically generated sequential data contain abundant repetitive structures. Thus, understanding, revealing, and utilizing combinatorial objects that reside in strings are of great significance in designing efficient string processing algorithms and data structures. <br /><br />Typical topics of interests include (but are not limited to):<br />- Combinatorial string problems and solutions;<br />- Pattern matching algorithms;<br />- Applied combinatorics on words;<br />- Text compression;<br />- Data structures for strings, labeled trees, and compressed text.<br /><br />This Special Issue contains&nbsp;five peer-reviewed papers.

Cited by 0Open access

QUASIGROUP STRING PROCESSING: PART 4

Smile Markovski, Verica Bakeva · Contributions Section of Natural Mathematical and Biotechnical Sciences · 2017

A b s t r a c t: Given a ¯nite alphabet A and a quasigroup operation ¤ on the set A, in earlier paper of ours we have de¯ned the quasigroup transformation E : A+ ! A+, where A+ is the set of all ¯nite strings with letters from A. Here we present several generalizations of the transformation E and we consider the conditions under which the transformed strings have uniform distributions of n-tuples of letters of A. The obtained results can be applied in cryptography, coding theory, de¯ning and improving pseudo random generators, and so on.

Cited by 0Open access

Incremental processing for string similarity join

Yanglan Gan, Guangwei Xu, Cairong Yan et al. · International Journal of Computational Science and Engineering · 2019

String similarity join is an essential operation of data quality management and a key step to find the value of data. An incremental processing framework for string similarity join is proposed in this paper. Compared with the batching processing model, it can avoid the heavy time cost and the space cost brought by the duplicate similarity computation among historical strings and is suitable for processing data streams. We implement two algorithms: Inc-Join and Inp-Join. Inc-Join runs on a stand-alone machine while Inp-Join runs on a cluster with Spark environment. The experimental results show that this incremental processing framework can reduce the amount of string matching without affecting the join accuracy. When the data quantity becomes large, Inp-Join can make full use of the advantage of parallel processing and obtain a better performance than Inc-Join.

Cited by 0

Space efficient algorithms for string processing

Jasbir Dhaliwal · RMIT Research Repository (RMIT University Library) · 2013

The suffix array (SA), which is an array containing the suffixes of a string sorted into lexicographical order, was introduced in the late eighties as a space efficient alternative to the suffix tree. It has since emerged as a useful data structure in string processing problems such as pattern matching, pattern discovery, and data compression.<br><br>The SA is often coupled with the longest-common-prefix (LCP) array that contains the length of the longest common prefixes between consecutive suffixes in the SA. When enhanced with the LCP array, the SA can provide efficient solutions to the above applications including a problem called pattern mining. To date, all the mining algorithms lie at either extreme of the efficiency spectrum: they are either fast and use enormous amounts of space, or they are compact and orders of magnitude slower. We present a mining algorithm that achieves the best of both these extremes, having runtime comparable to the fastest published algorithms while using less space than the most space efficient. <br><br>In all these applications, the construction of the SA --- also known as suffix sorting --- is one of the main computational bottlenecks. Most papers describing the SA assume the SA fits in RAM memory, limiting their applications. The fastest algorithms in this large memory suffix sorting category use powerful pointer copying heuristics to expedite suffix sorting. Several space efficient algorithms have emerged in the last five years, where the trend is to use as little RAM as possible. They do so by finding a clever way to trade runtime, or by using slow compressed data structures, or by using external memory (disk), or some combination of these techniques. In this thesis, we focus on improving the runtime of a space efficient algorithm due to Kärkkäinen by adapting the heuristics from large memory suffix sorting to a semi-external setting. <br><br>Also, pointer copying has been heavily used to speed up the construction of the SA, but not the LCP array. We also discuss our attempts of combining the pointer copying heuristics to an efficient LCP construction algorithm due to Kärkkäinen, Manzini and Puglisi.<br><br>The Burrows-Wheeler transform (BWT) was discovered independently of the SA, but it is now known that the two data structures are deeply linked. The BWT is central to practical compression tools such as szip and bzip2. Many papers have been published on constructing the BWT either in RAM or in external memory but few on inverting the BWT to obtain the original string --- in fact none in external memory. For larger datasets, the existing traditional approaches cannot be used to invert the BWT. In such cases, we have to use disk. We close the gap between theory and practice by examining the problem of inverting the BWT efficiently on disk. We provide a practical implementation of the only theoretical proposal for the problem by Ferragina, Gagie and Manzini. We also provide new, faster solutions to the problem based on simple scanning and compression techniques.

Cited by 0Open access

Application of ternary quasigroups to string processing

Nikolay Alekseevich Shchuchkin · Discrete Mathematics and Applications · 2025

Abstract We propose an algorithm for string processing which is based on finite ternary quasigroups. Ternary quasigroups are specified by three-dimensional matrices of a certain form. We prove sufficient conditions for simplicity and polynomial completeness of a finite ternary quasigroup. We also propose a method for deciding polynomial completeness of a finite ternary quasigroup based on the analysis of the corresponding three-dimensional matrix.

Cited by 0

String processing algorithms for plant layout problems

Jouko Seppänen, James M. Moore · International Journal of Production Research · 1975

SUMMARY Difficulties in recognition of planarity of graphs in the context of layout problems are reviewed. A problem oriented linear string representation for trees and the concept of grammar are then introduced as basic tools for symbolic computation of trees and planar graphs. A heuristic method to solve the layout problem is next discussed and the need of optimal algorithmic techniques is pointed out. Finally the application of list processing techniques for manipulation by computer is suggested.

Cited by 26

Towards efficient string processing of annotated events

VOGEL, CARL, FERNANDO, RAFAEL · Arrow@dit (Dublin Institute of Technology) · 2017

This paper explores the use of strings as models to effectively represent event data such as might be found in a document annotated with ISO-TimeML. We describe the translation of such data to strings, as well as a number of operations, such as superposition, which may be used to manipulate these strings in order to infer new information. Some advantages and limitations of the operations are discussed, including issues of over-generation, which can be mitigated though the use of suitable constraints. In particular, we look at how Allen Relations, which might be extracted from a document annotated with ISO-TimeML, can be understood as useful constraints, and translated to strings.

Cited by 0Open access