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

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.

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
PublisherSpringer-Verlag
Pages173-189
Number of pages17
ISBN (Print)9783030022266
DOIs
StatePublished - Jan 1 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

Other

Other15th European Performance Engineering Workshop, EPEW 2018
CountryFrance
CityParis
Period10/29/1810/30/18

Fingerprint

Discrete Event
Stochastic models
Stochastic Model
Ontology
Model
Security Model
Evaluation Model
Graphical Models
Fragment
Diagram
Framework

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Keefe, K., Feddersen, B., Rausch, M., Wright, R., & Sanders, W. H. (2018). An Ontology Framework for Generating Discrete-Event Stochastic Models. In A. Remke, P. Ballarini, B. Barbot, R. Bakhshi, & H. Castel-Taleb (Eds.), Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings (pp. 173-189). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11178 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-02227-3_12

An Ontology Framework for Generating Discrete-Event Stochastic Models. / Keefe, Ken; Feddersen, Brett; Rausch, Michael; Wright, Ronald; Sanders, William H.

Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. ed. / Anne Remke; Paolo Ballarini; Benoît Barbot; Rena Bakhshi; Hind Castel-Taleb. Springer-Verlag, 2018. p. 173-189 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11178 LNCS).

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

Keefe, K, Feddersen, B, Rausch, M, Wright, R & Sanders, WH 2018, An Ontology Framework for Generating Discrete-Event Stochastic Models. in A Remke, P Ballarini, B Barbot, R Bakhshi & H Castel-Taleb (eds), Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11178 LNCS, Springer-Verlag, pp. 173-189, 15th European Performance Engineering Workshop, EPEW 2018, Paris, France, 10/29/18. https://doi.org/10.1007/978-3-030-02227-3_12
Keefe K, Feddersen B, Rausch M, Wright R, Sanders WH. An Ontology Framework for Generating Discrete-Event Stochastic Models. In Remke A, Ballarini P, Barbot B, Bakhshi R, Castel-Taleb H, editors, Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. Springer-Verlag. 2018. p. 173-189. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-02227-3_12
Keefe, Ken ; Feddersen, Brett ; Rausch, Michael ; Wright, Ronald ; Sanders, William H. / An Ontology Framework for Generating Discrete-Event Stochastic Models. Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. editor / Anne Remke ; Paolo Ballarini ; Benoît Barbot ; Rena Bakhshi ; Hind Castel-Taleb. Springer-Verlag, 2018. pp. 173-189 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@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 = "1",
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",

}

TY - GEN

T1 - An Ontology Framework for Generating Discrete-Event Stochastic Models

AU - Keefe, Ken

AU - Feddersen, Brett

AU - Rausch, Michael

AU - Wright, Ronald

AU - Sanders, William H.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Discrete-event simulation

KW - Executable models

KW - Model generation

KW - Ontology

UR - http://www.scopus.com/inward/record.url?scp=85055524831&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055524831&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-02227-3_12

DO - 10.1007/978-3-030-02227-3_12

M3 - Conference contribution

AN - SCOPUS:85055524831

SN - 9783030022266

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 173

EP - 189

BT - Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings

A2 - Remke, Anne

A2 - Ballarini, Paolo

A2 - Barbot, Benoît

A2 - Bakhshi, Rena

A2 - Castel-Taleb, Hind

PB - Springer-Verlag

ER -