Discrete-Event Modeling Ontology (DeMO):
Information Sources

DeMO is an ontology for discrete-event modeling (DEM) (system dynamics for discrete-event systems (DES)). The models in the ontology focus on how state evolves over time. The state is typically discrete (finite or countable), while time may be continuous (uncountable), although the number of state changes (events) must be discrete. We also focus on stochastic models, although deterministic models are considered as a special case. It is hoped that the DeMO ontology will be useful to researcher and practitioners of modeling and simulation. Such an ontology could also find application in locating modeling and simulation software, particular modeling applications and modeling components as well as facilitating meta-modeling and multi-modeling. This page represents information sources being used to create the DeMO ontology.

Types of Models

• State Machines:

  • Finite State Automata: (Hopcroft, Motwani and Ullman)

  • I/O Automata: (Lynch and Tuttle)

  • Timed Automata: (Alur and Dill)

  • Probabilistic Automata: (Sagala)

• Generalized Semi-Markov Processes (GSMP): ( Haas and Shedler, 1987; Shedler, 1993)

  • Semi-Markov Processes (SMP):

  • Markov Chains (MC):

  • Hidden Markov Models (HMM):

• Petri Nets (PN):

  • Stochastic Petri Nets (SPN):

  • Decision Free Petri Nets:

• DEVS Models:

• Event Graphs (EG): (Schruben)

  • Extended Event Graphs (EEG):

• Activity Cycle Diagrams (ACD): Tocher

• Process Algebra (PA):

  • Stochastic Process Algebra (SPA): ( Hermanns, Herzog and Katoen)

  • Pi-Calculus

• Stochastic Task Graphs (STG)

Taxonomies and Ontologies

• Marson, Robbio and Donatelli, Petri Nets in Performance Evaluation: An Introduction

• Ciardo, German and Lindemann, A Characterization of the Stochastic Process underlying a Stochastic Petri Net

• Balci and Nance, A Taxonomy of Layout Composition Techniques for Visual Simulation

• Page, Formal Approaches to Discrete Event Simulation

• Frantz, A Taxonomy of Model Abstraction Techniques

• Fishwick and Zeigler, A Multimodel Methodology for Qualitative Model Engineering

• Fishwick, A Taxonomy for Simulation Modeling Based on Programming Language Principles

• Fishwick, Extending Object-Oriented Design for Physical Modeling

• Parallelisation & Intelligence Ontologies for Model Reuse

• Miller, Sheth and Kochut, Perspectives in Modeling: Simulation, Database and Workflow

Papers on Meta-Modeling and Multi-Modeling

• Vangheluwe, Lara and Mosterman, An Introduction to Multi-Pardigm Modeling and Simulation

• Fishwick, A Multimodel Methodology for Qualitative Model Engineering

Relevant Textbooks

• Fishwick, Simulation Model Design and Execution

• Zeigler,

• Cassandras, Discrete Event Systems: Modeling and Performance Analysis

• Cassandrasn and Lafortune, Introduction to Discrete Event Systems

Relevant Encyclopedias

• Wikipedia

• NIST Dictionary of Algorithms and Data Structures