On Parallel Induction of Nondeterministic Finite Automata

Jastrząb, Tomasz

doi:10.1016/j.procs.2016.05.318

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…Our experiments are conducted on state-of-the-art instances that vary in the sizes of the alphabet and the number of positive and negative words in the learning sample. We present a simplified version of the (k + 1)_NFA ⋆ model: indeed, the models briefly presented in Section 2.3 can be rewritten and simplified when combined with Constraints (6)(7)(8)(9)(10)(11), leading to SAT instances with fewer variables and constraints. We also compare the performance of the two models in terms of the number of solutions and running time.…”

Section: Experimentationmentioning

confidence: 99%

“…Some studies have employed metaheuristics, such as hill-climbing in [16], while others have used complete solvers that can always find a solution if one exists, prove unsatisfiability, and find the global optimum in optimization problems. The problem is often modeled as a Constraint Satisfaction Problem (CSP) and various techniques have been employed, such as Integer Non-Linear Programming (INLP) in [19] and parallel solvers in [6,7]. Additionally, the author of [8] proposed two strategies for solving the CSP formulation of the problem, and [9] presents a parallel approach for solving the optimization variant of the problem.…”

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confidence: 99%

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Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Jastrząb

Lardeux

Monfroy

2023

Lecture Notes in Computer Science

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Grammatical inference involves learning a formal grammar as a finite state machine or set of rewrite rules. This paper focuses on inferring Nondeterministic Finite Automata (NFA) from a given sample of words: the NFA must accept some words, and reject others. Our approach is unique in that it addresses the question of whether or not a finite automaton of size k exists for a given sample by using an overconstrained model of size k + 1. Additionally, our method allows for the identification of the automaton of size k when it exists. While the concept may seem straightforward, the effectiveness of this approach is demonstrated through the results of our experiments.

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Section: Experimentationmentioning

confidence: 99%

mentioning

confidence: 99%

Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Jastrząb

Lardeux

Monfroy

2023

Lecture Notes in Computer Science

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“…Constraints ( 8) are used for words from S + and Constraints (9) for words from S − . Efficient decomposition of each word into a prefix and a suffix is crucial.…”

Section: Models For K_nfa Inferencementioning

confidence: 99%

“…For example, in [3], an Integer Non-Linear Programming (INLP) formulation of the problem is given. Parallel solvers for minimizing the inferred NFA size are presented in [9], [10]. The author of [11] proposes two strategies, based on variable ordering, for solving the CSP formulation of the problem.…”

Section: Introductionmentioning

confidence: 99%

Taking Advantage of a Very Simple Property to Efficiently Infer NFAs

Jastrząb

Lardeux

Monfroy

2022

2022 IEEE 34th International Conference on Tools With Artificial Intelligence (ICTAI)

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Grammatical inference consists in learning a formal grammar as a finite state machine or as a set of rewrite rules. In this paper, we are concerned with inferring Nondeterministic Finite Automata (NFA) that must accept some words, and reject some other words from a given sample. This problem can naturally be modeled in SAT. The standard model being enormous, some models based on prefixes, suffixes, and hybrids were designed to generate smaller SAT instances.There is a very simple and obvious property that says: if there is an NFA of size k for a given sample, there is also an NFA of size k+1. We first strengthen this property by adding some characteristics to the NFA of size k+1. Hence, we can use this property to tighten the bounds of the size of the minimal NFA for a given sample. We then propose simplified and refined models for NFA of size k+1 that are smaller than the initial models for NFA of size k. We also propose a reduction algorithm to build an NFA of size k from a specific NFA of size k+1. Finally, we validate our proposition with some experimentation that shows the efficiency of our approach.

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“…At present, the main event detection models of CEP system include NFA detection model, FSM detection model, Petri network detection model, and detection model based on tree or graph [8]. Through investigation and analysis, CEP generally uses NFA detection model, as shown in Table 1.…”

Section: B the Analysis Of Cepmentioning

confidence: 99%

An Anomaly Detection Method for Stateful Stream Processing System

Chang¹,

Zhao²,

Liu³

et al. 2017

Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

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Abstract-The SSPS (stateful stream processing system) is a distributed complex event processing system implemented by the framework of stream processing system. However, due to the nature of uncertainty, randomness and burstiness of the data stream and the complexity of complex event processing, the SSPS faces great challenges. In order to address the problem, this paper analyzes the problems of SSPS, on this basis, an anomaly detection method based on FKPCA-SVM is proposed. Experimental results show that the proposed method can efficiently and reliably detect SSPS, and ensure the system can operate normally.

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On Parallel Induction of Nondeterministic Finite Automata

Cited by 8 publications

References 12 publications

Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Taking Advantage of a Very Simple Property to Efficiently Infer NFAs

An Anomaly Detection Method for Stateful Stream Processing System

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