MO (Modeling Ontology)

DEMO (Discrete Event Modeling Ontology)

 

Intro

Diagram

Formalisms

Connections

 

 


Introduction (still a ROUGH SKETCH).

 

DEMO is an ontology for discrete-event modeling (system dynamics for discrete systems).  Here DES is understood as an event-driven system with discrete states. System evolution can thus be described by a sequence of ordered pairs (state, time): {(s0, t0), (s1, t1), …} with an assumption that for each change of state sk® sk+1 there exists an event e (or a set of events E*), that caused this change.

 

Our first goal is to define a hierarchical (in some sense) set of relevant modeling formalisms and relationships between them. We start with a smaller set in order to develop necessary building tools and approaches.

 

The taxonomy can be established based on different criteria and it is not quite clear for us yet, which one will prove to be the most useful. Still, we feel that the first thing to do is to establish a hierarchy based on theoretical modeling power.  In other words we want the formalisms on top of the taxonomy to have larger classes of discrete-event systems that they can model compared to the formalisms below (see the diagram).

 

We start by placing two equivalent (in modeling power) formalisms on the top – Generalized Stochastic Petri Nets and Generalized Semi-Markov Process models.

 

There are three main components of the formalisms in consideration:

1.     Underlying graphical representation;

2.     Probabilistic transitions; (these may be also considered as a part of 1)

3.     Stochastic clock structure that introduces time in the model and is used as an input;

 

Stochastic clock structure is completely independent of 1 and 2. This suggests that the taxonomy can grow in (at least) two orthogonal directions: one based on the underlying graph and another based on stochastic clock. In other words one can view it as (at least) a two-dimensional hierarchy. In the first dimension it grows down by putting certain restrictions on graph properties: topology, set cardinalities, connectivity, probabilistic transitions, etc.  In the second the restrictions are applied to stochastic clock properties: clock distribution functions, clock rates, and so on.  (Note: The diagram we have right now does not reflect the two-dimensional structure yet, but rather both directions are jumbled up in one.)

 

Source files in Word document format: DeMo-intro.doc, DeMo-diagram.doc