An Ontology Framework for Generating Discrete-Event Stochastic Models

Ken Keefe, Brett Feddersen, Michael Rausch, Ronald Wright, William H. Sanders

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Discrete-event stochastic models are a widely used approach for studying the behavior of systems that have not been implemented or that it would be too costly to examine directly. Valuable analysis depends on carefully constructed, well-founded models, which are very difficult for humans to create. To address this problem, we propose a framework for generating detailed, low-level models from high-level, block-diagram-style graphical models. Our approach uses extensible, collaborative ontology libraries that contain information about the types of components in a system, the types of relationships that connect those components, and fragments of low-level models that can be constructed together based on the definition of a high-level system model. This framework has been implemented and used in several case studies. We describe the framework and how model generation works by examining its use to generate complex ADversary VIew Security Evaluation (ADVISE) models.

Original languageEnglish (US)
Title of host publicationComputer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings
EditorsAnne Remke, Paolo Ballarini, Benoît Barbot, Rena Bakhshi, Hind Castel-Taleb
Number of pages17
ISBN (Print)9783030022266
StatePublished - 2018
Event15th European Performance Engineering Workshop, EPEW 2018 - Paris, France
Duration: Oct 29 2018Oct 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11178 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th European Performance Engineering Workshop, EPEW 2018


  • Discrete-event simulation
  • Executable models
  • Model generation
  • Ontology

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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