Factor Oracle: A New Structure for Pattern Matching

Allauzen, Cyril; Crochemore, Maxime; Raffinot, Mathieu

doi:10.1007/3-540-47849-3_18

Cited by 113 publications

(106 citation statements)

References 11 publications

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“…The BNDM algorithm by Navarro and Raffinot [23] is based on the same principle, and combines it with suffix automata to find matches by efficiently identifying all subpatterns of a word. Another family of algorithms which relies on finding subpatterns is BOM [1] and its variations(cf. e.g.…”

Section: Related Workmentioning

confidence: 99%

Parallel String Matching

Pfaffe

Tillmann

Lutteropp

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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Abstract. We explore the benefits of parallelizing 7 state-of-the-art string matching algorithms. Using SIMD and multi-threading techniques we achieve a significant performance improvement of up to 43.3x over reference implementations and a speedup of up to 16.7x over the string matching program grep. We evaluate our implementations on the smart-corpora and the full human genome data set. We show scalability over number of threads and impact of pattern length.

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Section: Related Workmentioning

confidence: 99%

Parallel String Matching

Pfaffe

Tillmann

Lutteropp

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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“…Efficient variants of this approach analyze the pattern string to enable O( ) scanning that skips over regions of text whenever possible matches are provably not possible. This includes the widely studied KnuthMorris-Pratt [8], Boyer-Moore [25], and most recently, Factor Oracle [26] based algorithms.…”

Section: Secure Text Search Protocolsmentioning

confidence: 99%

Automata Evaluation and Text Search Protocols with Simulation-Based Security

2015

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This paper presents an efficient protocol for securely computing the fundamental problem of pattern matching. This problem is defined in the two-party setting, where party P 1 holds a pattern and party P 2 holds a text. The goal of P 1 is to learn where the pattern appears in the text, without revealing it to P 2 or learning anything else about P 2 's text.Our protocol is the first to address this problem with full security in the face of malicious adversaries. The construction is based on a novel protocol for secure oblivious automata evaluation which is of independent interest. In this problem, party P 1 holds an automaton and party P 2 holds an input string, and they need to decide if the automaton accepts the input, without learning anything else.

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“…In practice however, considering orders larger than 1 yields a better compromise, notably because there are many ways to represent efficiently the set of continuations for all possible prefixes of a given sequences set (such as prefix trees or Factor Oracles [2]). …”

Section: Various Species Of Markov Sequence Generationmentioning

confidence: 99%

“…This would necessitate the construction of the deterministic finite-state automaton corresponding to the language consisting of all subsequences of fixed length generated from the training set. Such an automaton can be produced for instance with tools borrowed from string matching, such as the Factor Oracle [2]. Note that such an automaton has been used for building music interactive systems using a random walk approach in the spirit of the Continuator [11].…”

Section: Propagators For Elementary Markov Constraintsmentioning

confidence: 99%

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Markov constraints: steerable generation of Markov sequences

Pachet¹,

Roy²

2010

Constraints

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Markov chains are a well known tool to model temporal properties of many phenomena, from text structure to fluctuations in economics. Because they are easy to generate, Markovian sequences, i.e. temporal sequences having the Markov property, are also used for content generation applications such as text or music generation that imitate a given style. However, Markov sequences are traditionally generated using greedy, left-to-right algorithms. While this approach is computationally cheap, it is fundamentally unsuited for interactive control. This paper addresses the issue of generating steerable Markovian sequences. We target interactive applications such as games, in which users want to control, through simple input devices, the way the system generates a Markovian sequence, such as a text, a musical sequence or a drawing. To this aim, we propose to revisit Markov sequence generation as a branch and bound constraint satisfaction problem (CSP). We propose a CSP formulation of the basic Markovian hypothesis as elementary Markov Constraints (EMC). We propose algorithms that achieve domain-consistency for the propagators of EMCs, in an event-based implementation of CSP. We show how EMCs can be combined to estimate the global Markovian probability of a whole sequence, and accommodate for different species of Markov generation such as fixed order, variable-order, or smoothing. Such a formulation, although more costly than traditional greedy generation algorithms, yields the immense advantage of being naturally steerable, since control specifications can be represented by arbitrary additional constraints, without any modification of the generation algorithm. We illustrate our approach on simple yet combinatorial chord sequence and melody generation problems and give some performance results.

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Factor Oracle: A New Structure for Pattern Matching

Cited by 113 publications

References 11 publications

Parallel String Matching

Parallel String Matching

Automata Evaluation and Text Search Protocols with Simulation-Based Security

Markov constraints: steerable generation of Markov sequences

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