Discrete-Event Modeling Ontology (DeMO):
Model Elements

 

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 preliminary information being used to create the DeMO ontology.

Warning - Rough Draft


Nomenclature

Model Primitives

Basic Sets

In discrete-event models, the state of a model changes at discrete points in time at which events occur, so all such models require the following three basic sets: State Space (S), Event Set (E) and Time Set (T). Together they form the S, E, T sets.

Initialization Function

Given the three basic sets (S, E, T), the model must be initialized to reflect appropriate starting conditions. In general, this can be done with an initialization function b, which will assign a value to the state at the beginning of time.

Driving Functions

Given the three basic sets and the initialization function (S, E, T, b) for a model, its dynamics (how it evolves over time) can be specified by two driving functions: one determining when events may occur and the other determining what happens when an event occurs. These are the Clock-Function (c) and the Transition-Function (d), respectively. Note, these definitions assume a time-homogeneous model where the laws governing the dynamics do not change over time; otherwise, time would need to be added to the domain for these functions.

Embellishments

Given the basic sets and functions (S, E, T, b, c, d) for a given model, one could visualize the evolution of the model by imagining one or more tokens moving around in the model. For Markov and automata models, it is natural to use one token to represent the current state. For other types of models (e.g., Petri Nets), multiple tokens may be required. In these types of models, a token resides at a given place until it is involved in a transition. Finally, tokens can be differentiated by using colors.

Composites


Categories

Dynamicity

Cardinality

Determinancy

Openness

Topology

Solutions