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!

Schreiben Sie den ersten Kommentar!