Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce

Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by...

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Vydáno v:Journal of big data Ročník 8; číslo 1; s. 1 - 27 / 145
Hlavní autoři: Elghadyry, Bilal, Ouardi, Faissal, Lotfi, Zineb, Verel, Sébastien
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 20.11.2021
Springer Nature B.V
Springer
SpringerOpen
Témata:
Algorithms
Artificial Intelligence
Big Data
Communications Engineering
Computational Science and Engineering
Computer Science
Conformance test
Cost analysis
Data Mining and Knowledge Discovery
Database Management
Derivation
Distinguishing
Experiments
Finite state machines
Information Storage and Retrieval
Machinery
MapReduce framework
Mathematical Applications in Computer Science
Networks
Parallel algorithm
Sequences
Conformance test
Parallel algorithm
Finite state machines
MapReduce framework
ISSN:2196-1115, 2196-1115
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Abstract Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.
AbstractList Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.
Abstract Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address the scalability issue encountered while deriving distinguishing sequences from complete observable nondeterministic finite state machines by introducing a massively parallel MapReduce version of the well-known Exact Algorithm. To the best of our knowledge, this is the first study to tackle this task using the MapReduce approach. First, we give a concise overview of the well-known Exact Algorithm for deriving distinguishing sequences from nondeterministic finite state machines. Second, we propose a parallel algorithm for this problem using the MapReduce approach and analyze its communication cost using Afrati et al. model. Furthermore, we conduct a variety of intensive and comparative experiments on a wide range of finite state machine classes to demonstrate that our proposed solution is efficient and scalable.
ArticleNumber 145
Author Ouardi, Faissal
Elghadyry, Bilal
Lotfi, Zineb
Verel, Sébastien
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Issue 1
Keywords Conformance test
Parallel algorithm
Finite state machines
MapReduce framework
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SSID ssj0001340564
Score 2.1909487
Snippet Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper, we address...
Abstract Distinguishing sequences are widely used in finite state machine-based conformance testing to solve the state identification problem. In this paper,...
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StartPage 1
SubjectTerms Algorithms
Artificial Intelligence
Big Data
Communications Engineering
Computational Science and Engineering
Computer Science
Conformance test
Cost analysis
Data Mining and Knowledge Discovery
Database Management
Derivation
Distinguishing
Experiments
Finite state machines
Information Storage and Retrieval
Machinery
MapReduce framework
Mathematical Applications in Computer Science
Networks
Parallel algorithm
Sequences
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Title Efficient parallel derivation of short distinguishing sequences for nondeterministic finite state machines using MapReduce
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