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 language | English (US) |
|---|---|
| Title of host publication | Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings |
| Editors | Anne Remke, Paolo Ballarini, Benoît Barbot, Rena Bakhshi, Hind Castel-Taleb |
| Publisher | Springer-Verlag |
| Pages | 173-189 |
| Number of pages | 17 |
| ISBN (Print) | 9783030022266 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | 15th European Performance Engineering Workshop, EPEW 2018 - Paris, France Duration: Oct 29 2018 → Oct 30 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11178 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 15th European Performance Engineering Workshop, EPEW 2018 |
|---|---|
| Country | France |
| City | Paris |
| Period | 10/29/18 → 10/30/18 |
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Keywords
- Discrete-event simulation
- Executable models
- Model generation
- Ontology
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
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 proceeding › Conference contribution
}
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 -