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Quantitative Evaluation of Systems [electronic resource] : 14th International Conference, QEST 2017, Berlin, Germany, September 5-7, 2017, Proceedings /

Contributor(s): Material type: TextTextSeries: Theoretical Computer Science and General Issues ; 10503Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017Description: XIII, 370 p. 94 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319663357
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 004.0151 23
LOC classification:
  • QA75.5-76.95
Online resources:
Contents:
Probabilistic modeling --  Smart energy systems over the cloud --  Petri nets and performance modeling --  Parametric verification -- Machine learning and formal methods -- Tools.  .
In: Springer Nature eBookSummary: This book constitutes the proceedings of  the 14th International Conference on Quantitative Evaluation Systems, QEST 2017, held in Berlin, Germany, in September 2017.The 20 full papers and 4 tool papers presented were carefully reviewed and selected From 58 submissions. The papers are organized in topical sections entitled: probabilistic modeling; smart energy systems over the cloud; Petri nets and performance modeling; parametric verification; machine learning and formal methods; tools.  .
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Probabilistic modeling --  Smart energy systems over the cloud --  Petri nets and performance modeling --  Parametric verification -- Machine learning and formal methods -- Tools.  .

This book constitutes the proceedings of  the 14th International Conference on Quantitative Evaluation Systems, QEST 2017, held in Berlin, Germany, in September 2017.The 20 full papers and 4 tool papers presented were carefully reviewed and selected From 58 submissions. The papers are organized in topical sections entitled: probabilistic modeling; smart energy systems over the cloud; Petri nets and performance modeling; parametric verification; machine learning and formal methods; tools.  .

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