Search results for "Suffix tree"

showing 10 items of 23 documents

On the Construction of Classes of Suffix Trees for Square Matrices: Algorithms and Applications

1996

AbstractWe provide a uniform framework for the study of index data structures for a two-dimensional matrixTEXT[1:n, 1:n] whose entries are drawn from an ordered alphabetΣ. An index forTEXTcan be informally seen as the two-dimensional analog of the suffix tree for a string. It allows on-line searches and statistics to be performed onTEXTby representing compactly theΘ(n3) square submatrices ofTEXTin optimalO(n2) space. We identify 4n−1families of indices forTEXT, each containing ∏ni=1(2i−1)! isomorphic data structures. We also develop techniques leading to a single algorithm that efficiently builds any index in any family inO(n2logn) time andO(n2) space. Such an algorithm improves in various …

Discrete mathematicsSuffix treeString (computer science)Generalized suffix treeBlock matrixData structureSquare matrixComputer Science ApplicationsTheoretical Computer Sciencelaw.inventionCombinatoricsComputational Theory and MathematicslawTree (set theory)SuffixInformation SystemsMathematics
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Adaptive learning of compressible strings

2020

Suppose an oracle knows a string $S$ that is unknown to us and that we want to determine. The oracle can answer queries of the form "Is $s$ a substring of $S$?". In 1995, Skiena and Sundaram showed that, in the worst case, any algorithm needs to ask the oracle $\sigma n/4 -O(n)$ queries in order to be able to reconstruct the hidden string, where $\sigma$ is the size of the alphabet of $S$ and $n$ its length, and gave an algorithm that spends $(\sigma-1)n+O(\sigma \sqrt{n})$ queries to reconstruct $S$. The main contribution of our paper is to improve the above upper-bound in the context where the string is compressible. We first present a universal algorithm that, given a (computable) compre…

FOS: Computer and information sciencesCentroid decompositionGeneral Computer ScienceString compressionAdaptive learningKolmogorov complexityContext (language use)Data_CODINGANDINFORMATIONTHEORYString reconstructionTheoretical Computer ScienceCombinatoricsString reconstruction; String learning; Adaptive learning; Kolmogorov complexity; String compression; Lempel-Ziv; Centroid decomposition; Suffix treeSuffix treeIntegerComputer Science - Data Structures and AlgorithmsOrder (group theory)Data Structures and Algorithms (cs.DS)Adaptive learning; Centroid decomposition; Kolmogorov complexity; Lempel-Ziv; String compression; String learning; String reconstruction; Suffix treeTime complexityComputer Science::DatabasesMathematicsLempel-ZivSettore INF/01 - InformaticaLinear spaceString (computer science)SubstringBounded functionString learningTheoretical Computer Science
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Uncommon Suffix Tries

2011

Common assumptions on the source producing the words inserted in a suffix trie with $n$ leaves lead to a $\log n$ height and saturation level. We provide an example of a suffix trie whose height increases faster than a power of $n$ and another one whose saturation level is negligible with respect to $\log n$. Both are built from VLMC (Variable Length Markov Chain) probabilistic sources; they are easily extended to families of sources having the same properties. The first example corresponds to a ''logarithmic infinite comb'' and enjoys a non uniform polynomial mixing. The second one corresponds to a ''factorial infinite comb'' for which mixing is uniform and exponential.

FOS: Computer and information sciencesCompressed suffix arrayPolynomialLogarithmGeneral MathematicsSuffix treevariable length Markov chain[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]Generalized suffix treeprobabilistic source0102 computer and information sciences02 engineering and technologysuffix trie01 natural scienceslaw.inventionCombinatoricslawComputer Science - Data Structures and AlgorithmsTrieFOS: Mathematics0202 electrical engineering electronic engineering information engineeringData Structures and Algorithms (cs.DS)Mixing (physics)[ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS]MathematicsDiscrete mathematicsApplied MathematicsProbability (math.PR)020206 networking & telecommunicationssuffix trie.Computer Graphics and Computer-Aided Design[MATH.MATH-PR]Mathematics [math]/Probability [math.PR]010201 computation theory & mathematicsmixing properties60J05 37E05Suffix[ MATH.MATH-PR ] Mathematics [math]/Probability [math.PR]Mathematics - ProbabilitySoftware
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The rightmost equal-cost position problem.

2013

LZ77-based compression schemes compress the input text by replacing factors in the text with an encoded reference to a previous occurrence formed by the couple (length, offset). For a given factor, the smallest is the offset, the smallest is the resulting compression ratio. This is optimally achieved by using the rightmost occurrence of a factor in the previous text. Given a cost function, for instance the minimum number of bits used to represent an integer, we define the Rightmost Equal-Cost Position (REP) problem as the problem of finding one of the occurrences of a factor whose cost is equal to the cost of the rightmost one. We present the Multi-Layer Suffix Tree data structure that, for…

FOS: Computer and information sciencesOffset (computer science)Computer scienceSuffix treeComputer Science - Information Theorylaw.inventionCombinatoricslawLog-log plotComputer Science - Data Structures and AlgorithmsCompression schemetext compressiondictionary text compressionData Structures and Algorithms (cs.DS)LZ77 compressiondata compressionLossless compressionfull text indexSuffix Tree Data StructuresSettore INF/01 - InformaticaInformation Theory (cs.IT)Data structurePrefixCompression ratioCompression scheme; Constant time; Suffix Tree Data StructuresAlgorithmData compressionConstant time
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From First Principles to the Burrows and Wheeler Transform and Beyond, via Combinatorial Optimization

2007

AbstractWe introduce a combinatorial optimization framework that naturally induces a class of optimal word permutations with respect to a suitably defined cost function taking into account various measures of relatedness between words. The Burrows and Wheeler transform (bwt) (cf. [M. Burrows, D. Wheeler, A block sorting lossless data compression algorithm, Technical Report 124, Digital Equipment Corporation, 1994]), and its analog for labelled trees (cf. [P. Ferragina, F. Luccio, G. Manzini, S. Muthukrishnan, Structuring labeled trees for optimal succinctness, and beyond, in: Proc. of the 45th Annual IEEE Symposium on Foundations of Computer Science, 2005, pp. 198–207]), are special cases i…

Lossless compressionBoosting (machine learning)General Computer ScienceComputer scienceComputationData_CODINGANDINFORMATIONTHEORYLyndon wordOptimal word permutationTheoretical Computer ScienceCombinatoricsPermutationSuffix treeCombinatorial optimizationBurrows–Wheeler transformTime complexityComputer Science(all)
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Suffix array and Lyndon factorization of a text

2014

Abstract The main goal of this paper is to highlight the relationship between the suffix array of a text and its Lyndon factorization. It is proved in [15] that one can obtain the Lyndon factorization of a text from its suffix array. Conversely, here we show a new method for constructing the suffix array of a text that takes advantage of its Lyndon factorization. The surprising consequence of our results is that, in order to construct the suffix array, the local suffixes inside each Lyndon factor can be separately processed, allowing different implementative scenarios, such as online, external and internal memory, or parallel implementations. Based on our results, the algorithm that we prop…

Sorting suffixes; BWT; Suffix array; Lyndon word; Lyndon factorizationCompressed suffix arraySettore INF/01 - InformaticaSorting suffixesGeneralized suffix treeSuffix arrayOrder (ring theory)Construct (python library)Lyndon wordSorting suffixeTheoretical Computer Sciencelaw.inventionBWTLyndon factorizationComputational Theory and MathematicsFactorizationlawSuffix arrayFactor (programming language)Internal memoryDiscrete Mathematics and CombinatoricsArithmeticcomputerMathematicscomputer.programming_languageJournal of Discrete Algorithms
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On-line Construction of Two-Dimensional Suffix Trees

1999

AbstractWe say that a data structure is builton-lineif, at any instant, we have the data structure corresponding to the input we have seen up to that instant. For instance, consider the suffix tree of a stringx[1,n]. An algorithm building iton-lineis such that, when we have read the firstisymbols ofx[1,n], we have the suffix tree forx[1,i]. We present a new technique, which we refer to asimplicit updates, based on which we obtain: (a) an algorithm for theon-lineconstruction of the Lsuffix tree of ann×nmatrixA—this data structure is the two-dimensional analog of the suffix tree of a string; (b) simple algorithms implementing primitive operations forLZ1-typeon-line losslessimage compression m…

Statistics and ProbabilityCompressed suffix arrayNumerical AnalysisControl and OptimizationAlgebra and Number TheoryTheoretical computer scienceApplied MathematicsGeneral MathematicsSuffix treeString (computer science)Generalized suffix treelaw.inventionLongest common substring problemTree (data structure)lawSuffixAlgorithmFM-indexMathematicsJournal of Complexity
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Parallel Construction and Query of Index Data Structures for Pattern Matching on Square Matrices

1999

AbstractWe describe fast parallel algorithms for building index data structures that can be used to gather various statistics on square matrices. The main data structure is the Lsuffix tree, which is a generalization of the classical suffix tree for strings. Given ann×ntext matrixA, we build our data structures inO(logn) time withn2processors on a CRCW PRAM, so that we can quickly processAin parallel as follows: (i) report some statistical information aboutA, e.g., find the largest repeated square submatrices that appear at least twice inAor determine, for each position inA, the smallest submatrix that occurs only there; (ii) given, on-line, anm×mpattern matrixPAT, check whether it occurs i…

Statistics and ProbabilityNumerical AnalysisControl and OptimizationAlgebra and Number TheoryApplied MathematicsGeneral MathematicsSuffix treeParallel algorithmData structureSquare matrixSquare (algebra)law.inventionTree (data structure)lawPattern matchingAlgorithmMathematicsData compressionJournal of Complexity
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Boosting Textual Compression in Optimal Linear Time

2005

We provide a general boosting technique for Textual Data Compression. Qualitatively, it takes a good compression algorithm and turns it into an algorithm with a better compression performance guarantee. It displays the following remarkable properties: (a) it can turn any memoryless compressor into a compression algorithm that uses the “best possible” contexts; (b) it is very simple and optimal in terms of time; and (c) it admits a decompression algorithm again optimal in time. To the best of our knowledge, this is the first boosting technique displaying these properties.Technically, our boosting technique builds upon three main ingredients: the Burrows--Wheeler Transform, the Suffix Tree d…

Theoretical computer scienceBurrows–Wheeler transformSuffix treeString (computer science)Data_CODINGANDINFORMATIONTHEORYBurrows-Wheeler transformSubstringArithmetic codinglaw.inventionLempel-Ziv compressorsArtificial IntelligenceHardware and ArchitectureControl and Systems Engineeringlawtext compressionempirical entropyArithmetic codingGreedy algorithmTime complexityAlgorithmSoftwareInformation SystemsMathematicsData compression
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Algorithmics for the Life Sciences

2013

The life sciences, in particular molecular biology and medicine, have wit- nessed fundamental progress since the discovery of the “the Double Helix”. A rele- vant part of such an incredible advancement in knowledge has been possible thanks to synergies with the mathematical sciences, on the one hand, and computer science, on the other. Here we review some of the most relevant aspects of this cooperation focusing on contributions given by the design, analysis and engineering of fast al- gorithms for the life sciences.

Theoretical computer scienceSettore INF/01 - InformaticaKolmogorov complexityMathematical scienceslawComputer scienceSuffix treeAlgorithmicsDesign and Analysis of Algorithms Bioinformaticslaw.invention
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