@inproceedings{78744ce58d2046a2a1c8add62abcd9c8,
title = "An Ontology Framework for Generating Discrete-Event Stochastic Models",
abstract = "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.",
keywords = "Discrete-event simulation, Executable models, Model generation, Ontology",
author = "Ken Keefe and Brett Feddersen and Michael Rausch and Ronald Wright and Sanders, {William H.}",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-030-02227-3_12",
language = "English (US)",
isbn = "9783030022266",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "173--189",
editor = "Anne Remke and Paolo Ballarini and Beno{\^i}t Barbot and Rena Bakhshi and Hind Castel-Taleb",
booktitle = "Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings",
note = "15th European Performance Engineering Workshop, EPEW 2018 ; Conference date: 29-10-2018 Through 30-10-2018",
}