Modeling Temporal Dynamics for Business Systems

Modeling Temporal Dynamics for Business Systems

Gove N. Allen (Tulane University, USA) and Salvatore T. March (Vanderbilt University, USA)
Copyright: © 2003 |Pages: 16
DOI: 10.4018/jdm.2003070102
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Abstract

Research in temporal database management has viewed temporal dynamics from a structural perspective, posing extensions to the entity-relationship (E-R) model to represent the state history of time-dependent attributes and relationships. We argue that temporal dynamics are semantic rather than structural and that the existing constructs in the E-R model are sufficient to represent them. Practitioners have long used E-R models without temporal extensions to design systems with rich support for temporality by modeling both things and events as entities — a practice that is consistent with the original presentation of the E-R model. This approach supports methodologies that leverage narrative and human cognitive processing capabilities in the development and verification of data models. Furthermore it maintains modeling parsimony and facilitates the representation of causality — why a particular state exists.

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