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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of big data Jg. 8; H. 1; S. 1 - 27 / 145
Hauptverfasser: Elghadyry, Bilal, Ouardi, Faissal, Lotfi, Zineb, Verel, Sébastien
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 20.11.2021
Springer Nature B.V
Springer
SpringerOpen
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-021-00535-6