Changes, States, and Events: The Thread from Staticity to Dynamism in the Conceptual Modeling of Systems

TL;DR

This paper explores how to model change, states, and events in systems using a new methodology called thinging machine (TM), transforming static models into dynamic, temporal representations to better understand system behavior.

Contribution

It introduces a novel TM-based modeling approach that extends static system descriptions into dynamic, temporal models by integrating states and events.

Findings

FSM states are static changes that lead to temporal events.

The TM methodology effectively captures system behavior through static and dynamic models.

Recasting static models into temporal ones enhances understanding of system dynamics.

Abstract

This paper examines the concept of change in conceptual modeling. Change is inherent in the nature of things and has increasingly become a focus of much interest and investigation. Change can be modeled as a transition between two states of a finite state machine (FSM). This change represents an exploratory starting point in this paper. Accordingly, a sample FSM that models a car s transmission system is re-expressed in terms of a new modeling methodology called thinging machine (TM) modeling. Recasting the car-transmission model involves developing (1) an S model that captures the static aspects, (2) a D model that identifies states, and (3) a B model that specifies the behavior. The analysis progresses as follows. - S represents an atemporal diagrammatic description that embeds underlying compositions (static changes) from which the roots of system behavior can be traced. - S is broken down into multiple subsystems that correspond to static states (ordered constitutive components). - Introducing time into static states converts these states into events, and the behavior (B) model is constructed based on the chronology of these events. The analysis shows that FSM states are static (atemporal) changes that introduce temporal events as carriers of behavior. This result enhances the semantics of the concepts of change, states, and events in modeling and shows how to specify a system s behavior through its static description.